Enough ink has been spilled on the topic to fill libraries, but this article is meant to distill some of the key arguments, viewpoints, and general information related to facial recognition systems and the impacts they can have on our privacy today.

What Are Facial Recognition Systems?

The actual technology behind FRS and who develops them can be complicated. It’s best to have a basic idea of how these systems work before diving into the ethical and privacy-related concerns related to using them.

How Do Facial Recognition Systems Work?

On a basic level, facial recognition systems operate on a three-step process. First, the hardware, such as a security camera or smartphone, records a photo or video of a person.

That photo or video is then fed into an AI program, which then maps and analyzes the geometry of a person’s face, such as the distance between eyes or the contours of the face. The AI also identifies specific facial landmarks, like forehead, eye sockets, eyes, or lips.

Finally, all these landmarks and measurements come together to create a digital signature which the AI compares against its database of digital signatures to see if there is a match or to verify someone’s identity. That digital signature is then stored on the database for future reference.

Read More At: The Pros and Cons of Enlisting AI for Cybersecurity

Use Cases of Facial Recognition Systems

A technology like facial recognition is broadly applicable to a number of different industries. Two of the most obvious are law enforcement and security. 

With facial recognition software, law enforcement agencies can track suspects and offenders unfortunate enough to be caught on camera, while security firms can utilize it as part of their access control measures, checking people’s faces as easily as they check people’s ID cards or badges.

Access control in general is the most common use case for facial recognition so far. It generally relies on a smaller database (i.e. the people allowed inside a specific building), meaning the AI is less likely to hit a false positive or a similar error. Plus, it’s such a broad use case that almost any industry imaginable could find a reason to implement the technology.

Facial recognition is also a hot topic in the education field, especially in the U.S. where vendors pitch facial recognition surveillance systems as a potential solution to the school shootings that plague the country more than any other. It has additional uses in virtual classroom platforms as a way to track student activity and other metrics.

In healthcare, facial recognition can theoretically be combined with emergent tech like emotion recognition for improved patient insights, such as being able to detect pain or monitor their health status. It can also be used during the check-in process as a no-contact alternative to traditional check-in procedures.

The world of banking saw an increase in facial recognition adoption during the COVID-19 pandemic, as financial institutions looked for new ways to safely verify customers’ identities.

Some workplaces already use facial recognition as part of their clock-in-clock-out procedures. It’s also seen as a way to monitor employee productivity and activity, preventing folks from “sleeping on the job,” as it were. 

Companies like HireVue were developing software using facial recognition that can determine the hireability of prospective employees. However, it discontinued the facial analysis portion of its software in 2021. In a statement, the firm cited public concerns over AI and a growing devaluation of visual components to the software’s effectiveness.

Retailers who sell age-restricted products, such as bars or grocery stores with liquor licenses, could use facial recognition to better prevent underaged customers from buying these products.

Who Develops Facial Recognition Systems?

The people developing FRS are many of the same usual suspects who push other areas of tech research forward. As always, academics are some of the primary contributors to facial recognition innovation. The field was started in academia in the 1950s by researchers like Woody Bledsoe.

In a modern day example, The Chinese University of Hong Kong created the GaussianFace algorithm in 2014, which its researchers reported had surpassed human levels of facial recognition. The algorithm scored 98.52% accuracy compared to the 97.53% accuracy of human performance.

In the corporate world, tech giants like Google, Facebook, Microsoft, IBM, and Amazon have been just some of the names leading the charge.

Google’s facial recognition is utilized in its Photos app, which infamously mislabeled a picture of software engineer Jacky Alciné and his friend, both of whom are black, as “gorillas” in 2015. To combat this, the company simply blocked “gorilla” and similar categories like “chimpanzee” and “monkey” on Photos.

Amazon was even selling its facial recognition system, Rekognition, to law enforcement agencies until 2020, when they banned the use of the software by police. The ban is still in effect as of this writing.

Facebook used facial recognition technology on its social media platform for much of the platform’s lifespan. However, the company shuttered the software in late 2021 as “part of a company-wide move to limit the use of facial recognition in [its] products.”

Additionally, there are firms who specialize in facial recognition software like Kairos, Clearview AI, and Face First who are contributing their knowledge and expertise to the field.

Read More At: The Value of Emotion Recognition Technology

Is This a Problem?

To answer the question of “should we be worried about facial recognition systems,” it will be best to look at some of the arguments that proponents and opponents of facial recognition commonly use.

Why Use Facial Recognition?

The most common argument in favor of facial recognition software is that it provides more security for everyone involved. In enterprise use cases, employers can better manage access control, while lowering the chance of employees becoming victims of identity theft.

Law enforcement officials say the use of FRS can aid their investigative abilities to make sure they catch perpetrators quickly and more accurately. It can also be used to track victims of human trafficking, as well as individuals who might not be able to communicate such as people with dementia. This, in theory, could reduce the number of police-caused deaths in cases involving these individuals.

Human trafficking and sex-related crimes are an oft-spoken refrain from proponents of this technology in law enforcement. Vermont, the state with the strictest bans on facial recognition, peeled back their ban slightly to allow for its use in investigating child sex crimes.

For banks, facial recognition could reduce the likelihood and frequency of fraud. With biometric data like what facial recognition requires, criminals can’t simply steal a password or a PIN and gain full access to your entire life savings. This would go a long way in stopping a crime for which the FTC received 2.8 million reports from consumers in 2021 alone.

Finally, some proponents say, the technology is so accurate now that the worries over false positives and negatives should barely be a concern. According to a 2022 report by the National Institute of Standards and Technology, top facial recognition algorithms can have a success rate of over 99%, depending on the circumstances.

With accuracy that good and use cases that strong, facial recognition might just be one of the fairest and most effective technologies we can use in education, business, and law enforcement, right? Not so fast, say the technology’s critics.

Why Ban Facial Recognition Technology?

First, the accuracy of these systems isn’t the primary concern for many critics of FRS. Whether the technology is accurate or not is inessential. 

While Academia is where much research on facial recognition is conducted, it is also where many of the concerns and criticisms are raised regarding the technology’s use in areas of life such as education or law enforcement. 

Northeastern University Professor of Law and Computer Science Woodrow Hartzog is one of the most outspoken critics of the technology. In a 2018 article Hartzog said, “The mere existence of facial recognition systems, which are often invisible, harms civil liberties, because people will act differently if they suspect they’re being surveilled.”

The concerns over privacy are numerous. As AI ethics researcher Rosalie A. Waelen put it in a 2022 piece for AI & Ethics, “[FRS] is expected to become omnipresent and able to infer a wide variety of information about a person.” The information it is meant to infer is not necessarily information an individual is willing to disclose.

Facial recognition technology has demonstrated difficulties identifying individuals of diverse races, ethnicities, genders, and age. This, when used by law enforcement, can potentially lead to false arrests, imprisonments, and other issues.

As a matter of fact, it already has. In Detroit, Robert Williams, a black man, was incorrectly identified by facial recognition software as a watch thief and falsely arrested in 2020. After being detained for 30 hours, he was released due to insufficient evidence after it became clear that the photographed suspect and Williams were not the same person.

This wasn’t the only time this happened in Detroit either. Michael Oliver was wrongly picked by facial recognition software as the one who threw a teacher’s cell phone and broke it.

A similar case happened to Nijeer Parks in late 2019 in New Jersey. Parks was detained for 10 days for allegedly shoplifting candy and trying to hit police with a car. Facial recognition falsely identified him as the perpetrator, despite Parks being 30 miles away from the incident at the time. 

There is also, in critics’ minds, an inherently dehumanizing element to facial recognition software and the way it analyzes the individual. Recall the aforementioned incident wherein Google Photos mislabeled Jacky Alciné and his friend as “gorillas.” It didn’t even recognize them as human. Given Google’s response to the situation was to remove “gorilla” and similar categories, it arguably still doesn’t.

Finally, there comes the issue of what would happen if the technology was 100% accurate. The dehumanizing element doesn’t just go away if Photos can suddenly determine that a person of color is, in fact, a person of color. 

The way these machines see us is fundamentally different from the way we see each other because the machines’ way of seeing goes only one way.  As Andrea Brighenti said, facial recognition software “leads to a qualitatively different way of seeing … .[the subject is] not even fully human. Inherent in the one way gaze is a kind of dehumanization of the observed.”

In order to get an AI to recognize human faces, you have to teach it what a human is, which can, in some cases, cause it to take certain human characteristics outside of its dataset and define them as decidedly “inhuman.”

That said, making facial recognition technology more accurate for detecting people of color only really serves to make law enforcement and business-related surveillance better. This means that, as researchers Nikki Stevens and Os Keyes noted in their 2021 paper for academic journal Cultural Studies, “efforts to increase representation are merely efforts to increase the ability of commercial entities to exploit, track and control people of colour.”

Final Thoughts

Ultimately, how much one worries about facial recognition technology comes down to a matter of trust. How much trust does a person place in the police or Amazon or any random individual who gets their hands on this software and the power it provides that they will only use it “for the right reasons”?

This technology provides institutions with power, and when thinking about giving power to an organization or an institution, one of the first things to consider is the potential for abuse of that power. For facial recognition, specifically for law enforcement, that potential is quite large.

In an interview for this article, Frederic Lederer, William & Mary Law School Chancellor Professor and Director of the Center for Legal & Court Technology, shared his perspective on the potential abuses facial recognition systems could facilitate in the U.S. legal system:

“Let’s imagine we run information through a facial recognition system, and it spits out 20 [possible suspects], and we had classified those possible individuals in probability terms. We know for a fact that the system is inaccurate and even under its best circumstances could still be dead wrong.

If what happens now is that the police use this as a mechanism for focusing on people and conducting proper investigation, I recognize the privacy objections, but it does seem to me to be a fairly reasonable use.

The problem is that police officers, law enforcement folks, are human beings. They are highly stressed and overworked human beings. And what little I know of reality in the field suggests that there is a large tendency to dump all but the one with the highest probability, and let’s go out and arrest him.”

Professor Lederer believes this is a dangerous idea, however:

“…since at minimum the way the system operates, it may be effectively impossible for the person to avoid what happens in the system until and unless… there is ultimately a conviction.”

Lederer explains that the Bill of Rights guarantees individuals a right to a “speedy trial.” However, court interpretations have borne out that arrested individuals will spend at least a year in prison before the courts even think about a speedy trial.

Add to that plea bargaining:

“…Now, and I don’t have the numbers, it is not uncommon for an individual in jail pending trial to be offered the following deal: ‘plead guilty, and we’ll see you’re sentenced to the time you’ve already been [in jail] in pre-trial, and you can walk home tomorrow.’ It takes an awful lot of guts for an individual to say ‘No, I’m innocent, and I’m going to stay here as long as is necessary.’

So if, in fact, we arrest the wrong person, unless there is painfully obvious evidence that the person is not the right person, we are quite likely to have individuals who are going to serve long periods of time pending trial, and a fair number of them may well plead guilty just to get out of the process.

So when you start thinking about facial recognition error, you can’t look at it in isolation. You have to ask: ‘How will real people deal with this information and to what extent does this correlate with everything else that happens?’ And at that point, there’s some really good concerns.”

As Lederer pointed out, these abuses already happen in the system, but facial recognition systems could exacerbate these abuses and even increase them. They can perpetuate pre-existing biases and systemic failings, and even if their potential benefits are enticing, the potential harm is too present and real to ignore.

Of the viable use cases of facial recognition that have been explored, the closest thing to a “safe” use case is ID verification. However, there are plenty of equally effective ID verification methods, some of which use biometrics like fingerprints.

In reality, there might not be any “safe” use case for facial recognition technology. Any advancements in the field will inevitably aid surveillance and control functions that have been core to the technology from its very beginning.

For now, Lederer said he hasn’t come to any firm conclusions as to whether the technology should be banned. But he and privacy advocates like Hartzog will continue to watch how it’s used.

Read Next: What’s Next for Ethical AI?

The post The Toll Facial Recognition Systems Might Take on Our Privacy and Humanity appeared first on IT Business Edge.

A decade from now, the combination of AI and 5G networks will have revolutionized how business gets done in our everyday lives.

Consumers will interact with companies through their personal AI assistants and 5G-enabled devices, physical and virtual, and demand information quickly and efficiently. They’ll receive this requested information almost instantaneously due to the vast bandwidth provided by 5G.

This high-speed data connection will open up new opportunities.

What is 5G?

5G is the fifth-generation mobile network. It is a set of standards for telecommunications and wireless communication protocols. In addition, it can provide higher speed, ultra-low latency, more comprehensive coverage, and more capacity than previous network generations.

What is Artificial Intelligence?

Artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. It’s a broad term referring to computer systems that mimic human thought processes. The cognitive processes replicated by these computer programs include learning, reasoning, and self-correction.

Also read: Labor Shortage: Is AI the Silver Bullet?

Potential 5G and AI Uses

While it’s still early, there are already a few applications for combining 5G and AI technologies.

5G-enabled autonomous vehicles

Having connected cars on a single network would help eliminate the issue with dead zones. If your phone drops a call when you drive under an overpass or through specific tunnels, imagine how much worse it would be if you were driving an autonomous vehicle.

The combination of fast network speeds with onboard sensors could enable self-driving cars to communicate with each other in real time about traffic conditions, potholes, accidents, or other road hazards.

Additionally, cities and transportation agencies could use that data to improve infrastructure and optimize traffic flow—for example, by identifying areas where adding new lanes or rerouting traffic might make sense.

AI-driven tools for service operations

AI-driven technologies help network engineers automate and optimize network activities and business continuity planning, from reporting issues to reacting to events and incidents.

For example, mobile networks and AI are merging in a new form of automation called AIOps. This approach is already being used by telecommunication companies to empower software tools to act quickly and respond immediately in the event of any operational events or incidents, security issues, or both, all without the need for human intervention.

Virtual reality (VR) and augmented reality (AR)

Both VR and AR rely on high-speed networks to deliver realistic images and sounds. With better connections, we’ll see higher resolution graphics and faster response times, which will lead to better experiences overall.

For example, a low latency connection won’t matter if your VR headset lags behind your head movements because it won’t take as long for image updates to reach your eyes. However, some industry experts believe 5G’s ultra-low latency may be critical to making VR and AR mainstream.

Also read: How Will 5G Change Augmented Reality?

Analyzing logs of data with AI

There will be a massive increase in the amount of data generated by IoT (Internet of Things) devices, servers, apps, network controllers, and other equipment due to the deployment of the 5G network. Unfortunately, there is little accessibility with conventional methods used to collect data in logs.

However, it is now possible for network management systems to be automated to analyze data, get results, and extract insights to improve network performance regularly, thereby decreasing downtime.

Utilities and energy

We’ve already seen a lot of interest in 5G-connected home appliances, including refrigerators and washing machines. Imagine a smart refrigerator that lets you know when your milk or eggs are going bad, so you don’t waste food.

Add AI to that mix, and suddenly your fridge will be able to order replacement items. Likewise, that same AI could tell your washer/dryer combo to run only after electricity rates drop to off-peak levels, potentially saving money on utility bills.

Also read: IoV: The Pioneering Union of IoT and the Automotive Industry

How Does 5G Help AI?

Advances in network technology like 5G could lead to greater speed and increased power efficiency for connected devices, which is crucial for developing self-learning systems.

As more and more devices connect to autonomous networks, more data will be created. The speed at which we can transfer data from one device to another has been a significant factor in how machine learning (ML) algorithms have evolved, helping them learn faster.

These advancements might even help us progress on some of AI’s biggest challenges, such as making it easier for machines to understand natural language and creating systems that can identify objects without being fed information by humans independently.

Here are three ways 5G could improve our future with AI:

Increased speed

Networking speeds determine how quickly computers can communicate with each other. This affects everything from latency times to processing speeds and energy consumption. In an age where connected devices are becoming increasingly common, these factors matter more.

Today, data transfer speeds over 4G networks average around 100 Mbps, while 5G promises up to 10 Gbps—an improvement of about 100 times faster. For AI, faster communication between devices means faster data transfer between processors, which translates into better responsiveness and higher levels of interactivity.

Additionally, faster response times allow for quicker feedback loops during training, meaning ML models can adapt to real-time changes rather than wait until their next scheduled session. It also makes it possible for machines to respond much more quickly if something goes wrong.

Reduced power consumption

Today’s mobile devices typically use two different kinds of wireless connectivity: cellular and Wi-Fi. Cellular connections are usually high-speed, but they consume more power because your phone needs to connect directly to a cell tower. On the other hand, Wi-Fi consumes less power because you can connect wirelessly to any available router, but its connection speeds tend to be slower.

5G networks promise lower latency times and longer battery life. One way this works is through beamforming, which allows 5G devices to transmit signals directly toward receivers rather than broadcasting them out in all directions. This reduces power consumption, allowing devices to be more efficient and get more out of a single charge.

Improved security

As 5G networks become more widespread, cybersecurity will become a bigger concern for consumers and companies. A recent report from Cybersecurity Ventures predicts that cyber crime will cost the world $10.5 trillion annually by 2025, so it’s no surprise that companies are starting to invest more in security.

5G networks will offer several benefits for cybersecurity, including faster data transfer speeds and improved encryption. For example, with 5G, it will be easier to transfer data from one connected device to another, making it faster and more secure for companies to share data between their employees. Likewise, 5G networks include an additional layer of encryption that protects data from hackers.

Also read: The Future of Natural Language Processing is Bright

AI and 5G are Enhancing Each Other’s Capabilities

Many envision a future where AI services work in conjunction with 5G networks, ensuring enhanced network speed doesn’t get bogged down by traffic. As companies become more reliant on cloud-based apps, they won’t have to worry about latency or service hiccups.

AI can analyze data gathered from 5G networks, providing valuable insights for businesses looking to improve their offerings. These two technologies are inextricably linked. Applying AI to both 5G networks and devices will increase efficiency and productivity across industries.

Millions of devices rely on speedy connections to receive information in today’s connected world. But 5G isn’t just speed—volume is about volume. The IoT devices worldwide are projected to amount to 30.9 billion units by 2025. Traditional network speeds won’t be able to handle them.

That’s where artificial intelligence comes in. Thanks to AI, networks can learn how best to deliver data to individual users based on their unique preferences and needs. So, while 5G provides a fast lane for massive amounts of data, artificial intelligence helps ensure every single piece of data gets where it needs to go as quickly as possible.

It’s an ideal pairing; by working together, these two technologies deliver better experiences for enterprises and consumers alike.

The Future Convergence of AI and 5G

As we think about how AI converges with other disruptive technologies, such as big data, cloud computing, blockchain, robotics, and IoT, converged systems have a distinct advantage over isolated systems.

The convergence of these two disruptive technologies can help businesses optimize their operations by making better decisions faster than ever before possible. These trends are already beginning to impact our daily lives through applications such as digital assistants, self-driving cars, and smart cities.

Combining artificial intelligence and 5G has many benefits in enterprise scenarios, including improving real-time analytics using ML techniques that enhance cybersecurity monitoring and protection, decision support for real-time actions and initiatives, predictive maintenance, and reducing network latency in business-critical applications.

Read next: Top Artificial Intelligence (AI) Software 2022

The post 5G and AI: Ushering in New Tech Innovation appeared first on IT Business Edge.

This is where MLOps comes in. MLOps is the practice of optimizing the continuous delivery of ML models, and it brings a host of benefits to organizations.

Below we explore the definition of MLOps, its benefits, and how it compares to AIOps. We also look at some of the top MLOps tools and platforms.

What Is MLOps?

MLOps combines machine learning and DevOps to automate, track, pipeline, monitor, and package machine learning models. It began as a set of best practices but slowly morphed into an independent ML lifecycle management approach. As a result, it applies to the entire lifecycle, from integrating data and model building to the deployment of models in a production environment.

MLOps is a special type of ModelOps, according to Gartner. However, MLOps is concerned with operationalizing machine learning models, whereas ModelOps focuses on all sorts of AI models.

Benefits of MLOps

The main benefits of MLOps are:

• Faster time to market: By automating deploying and monitoring models, MLOps enables organizations to release new models more quickly.
• Improved accuracy and efficiency: MLOps helps improve models’ accuracy by tracking and managing the entire model lifecycle. It also enables organizations to identify and fix errors more quickly.
• Greater scalability: MLOps makes it easier to scale up or down the number of machines used for training and inference.
• Enhanced collaboration: MLOps enables different teams (data scientists, engineers, and DevOps) to work together more effectively.

MLOps vs. AIOps: What are the Differences?

AIOps is a newer term coined in response to the growing complexity of IT operations. It refers to the application of artificial intelligence (AI) to IT operations, and it offers several benefits over traditional monitoring tools.

So, what are the key differences between MLOps and AIOps?

• Scope: MLOps is focused specifically on machine learning, whereas AIOps is broader and covers all aspects of IT operations.
• Automation: MLOps is largely automated, whereas AIOps relies on human intervention to make decisions.
• Data processing: MLOps uses pre-processed data for training models, whereas AIOps processes data in real time.
• Decision-making: MLOps relies on historical data to make decisions, whereas AIOps can use real-time data.
• Human intervention: MLOps requires less human intervention than AIOps.

Types of MLOps Tools

MLOps tools are divided into four major categories dealing with:

1. Data management
2. Modeling
3. Operationalization
4. End-to-end MLOps platforms

Data management

• Data Labeling: Large quantities of data, such as text, images, or sound recordings, are labeled using data labeling tools (also known as data annotation, tagging, or classification software). Labeled information is fed into supervised ML algorithms to generate new, unclassified data predictions.
• Data Versioning: Data versioning ensures that different versions of data are managed and tracked effectively. This is important for training and testing models as well as for deploying models into production.

Modeling

• Feature Engineering: Feature engineering is the process of transforming raw data into a form that is more suitable for machine learning algorithms. This can involve, for example, extracting features from data, creating dummy variables, or transforming categorical data into numerical features.
• Experiment Tracking: Experiment tracking enables you to keep track of all the steps involved in a machine learning experiment, from data preparation to model selection to final deployment. This helps to ensure that experiments are reproducible and the same results are obtained every time.
• Hyperparameter Optimization: Hyperparameter optimization is the process of finding the best combination of hyperparameters for an ML algorithm. This is done by running multiple experiments with different combinations of hyperparameters and measuring the performance of each model.

Operationalization

• Model Deployment/Serving: Model deployment puts an ML model into production. This involves packaging the model and its dependencies into a format that can be run on a production system.
• Model Monitoring: Model monitoring is tracking the performance of an ML model in production. This includes measuring accuracy, latency, and throughput and identifying any problems.

End-to-end MLOps platforms

Some tools go through the machine learning lifecycle from end to end. These tools are known as end-to-end MLOps platforms. They provide a single platform for data management, modeling, and operationalization. In addition, they automate the entire machine learning process, from data preparation to model selection to final deployment.

Also read: Top Observability Tools & Platforms

Best MLOps Tools & Platforms

Below are five of the best MLOps tools and platforms.

SuperAnnotate: Best for data labeling & versioning

Superannotate is used for creating high-quality training data for computer vision and natural language processing. The tool enables ML teams to generate highly precise datasets and effective ML pipelines three to five times faster with sophisticated tooling, QA (quality assurance), ML, automation, data curation, strong SDK (software development kit), offline access, and integrated annotation services.

In essence, it provides ML teams with a unified annotation environment that offers integrated software and service experiences that result in higher-quality data and faster data pipelines.

Key Features

• Pixel-accurate annotations: A smart segmentation tool allows you to separate images into numerous segments in a matter of seconds and create clear-cut annotations.
• Semantic and instance segmentation: Superannotate offers an efficient way to annotate Label, Class, and Instance data.
• Annotation templates: Annotation templates save time and improve annotation consistency.
• Vector Editor: The Vector Editor is an advanced tool that enables you to easily create, edit, and manage image and video annotations.
• Team communication: You can communicate with team members directly in the annotation interface to speed up the annotation process.

Pros

• Easy to learn and user-friendly
• Well-organized workflow
• Fast compared to its peers
• Enterprise-ready platform with advanced security and privacy features
• Discounts as your data volume grows

Cons

• Some advanced features such as advanced hyperparameter tuning and data augmentation are still in development.

Pricing

Superannotate has two pricing tiers, Pro and Enterprise. However, actual pricing is only available by contacting the sales team.

Iguazio: Best for feature engineering

Iguazio helps you build, deploy, and manage applications at scale.

New feature creation based on batch processing necessitates a tremendous amount of effort for ML teams. These features must be utilized during both the training and inference phases.

Real-time applications are more difficult to build than batch ones. This is because real-time pipelines must execute complex algorithms in real-time.

With the growing demand for real-time applications such as recommendation engines, predictive maintenance, and fraud detection, ML teams are under a lot of pressure to develop operational solutions to the problems of real-time feature engineering in a simple and reproducible manner.

Iguazio overcomes these issues by providing a single logic for generating real-time and offline features for training and serving. In addition, the tool comes with a rapid event processing mechanism to calculate features in real time.

Key Features

• Simple API to create complex features: Allows your data science staff to construct sophisticated features with a basic API (application programming interface) and minimize effort duplication and engineering resources waste. You can easily produce sliding windows aggregations, enrich streaming events, solve complex equations, and work on live-streaming events with an abstract API.
• Feature Store: Iguazio’s Feature Store provides a fast and reliable way to use any feature immediately. All features are stored and managed in the Iguazio integrated feature store.
• Ready for production: Remove the need to translate code and break down the silos between data engineers and scientists by automatically converting Python features into scalable, low-latency production-ready functions.
• Real-time graph: To easily make sense of multi-step dependencies, the tool comes with a real-time graph with built-in libraries for common operations with only a few lines of code.

Pros

• Real-time feature engineering for machine learning
• It eliminates the need for data scientists to learn how to code for production deployment
• Simplifies the data science process
• Highly scalable and flexible

Cons

• Iguazio has poor documentation compared to its peers.

Pricing

Iguazio offers a 14-day free trial but doesn’t publish any other pricing information on its website.

Neptune.AI: Best for experiment tracking

Neptune.AI is a tool that enables you to keep track of all your experiments and their results in one place. You can use it to monitor the performance of your models and get alerted when something goes wrong. With Neptune, you can log, store, query, display, categorize, and compare all of your model metadata in one place.

Key Features

• Full model building and experimentation control: Neptune.AI offers a single platform to manage all the stages of your machine learning models, from data exploration to final deployment. You can use it to keep track of all the different versions of your models and how they perform over time.
• Single dashboard for better ML engineering and research: You can use Neptune.AI’s dashboard to get an overview of all your experiments and their results. This will help you quickly identify which models are working and which ones need more adjustments. You can also use the dashboard to compare different versions of your models. Results, dashboards, and logs can all be shared with a single link.
• Metadata bookkeeping: Neptune.AI tracks all the important metadata associated with your models, such as the data they were trained on, the parameters used, and the results they produced. This information is stored in a searchable database, making it easy to find and reuse later. This frees up your time to focus on machine learning.
• Efficient use of computing resources: Neptune.AI allows you to identify under-performing models and save computing resources quickly. You can also reproduce results, making your models more compliant and easier to debug. In addition, you can see what each team is working on and avoid duplicating expensive training runs.
• Reproducible, compliant, and traceable models: Neptune.AI produces machine-readable logs that make it easy to track the lineage of your models. This helps you know who trained a model, on what data, and with what settings. This information is essential for regulatory compliance.
• Integrations: Neptune.AI integrates with over 25 different tools, making it easy to get started. You can use the integrations to pipe your data directly into Neptune.AI or to output your results in a variety of formats. In addition, you can use it with popular data science frameworks such as TensorFlow, PyTorch, and scikit-learn.

Pros

• Keeps track of all the important details about your experiments
• Tracks numerous experiments on a single platform
• Helps you to identify under-performing models quickly
• Saves computing resources
• Integrates with numerous data science tools
• Fast and reliable

Cons

• The user interface needs some improvement.

Pricing

Neptune.AI offers four pricing tiers as follows:

• Individual: Free for one member and includes a free quota of 200 monitoring hours per month and 100GB of metadata storage. Usage above the free quota is charged.
• Team: Costs $49 per month with a 14-day free trial. This plan allows unlimited members and has a free quota of 200 monitoring hours per month and 100GB of metadata storage. Usage above the free quota is charged. This plan also comes with email and chat support.
• Scale: With this tier, you have the option of SaaS (software as a service) or hosting on your infrastructure (annual billing). Pricing starts at $499 per month and includes unlimited members, custom metadata storage, custom monitoring hours quota, service accounts for CI workflows, single sign-on (SSO), onboarding support, and a service-level agreement (SLA).
• Enterprise: This plan is hosted on your infrastructure. Pricing starts at $1,499 per month (billed annually) and includes unlimited members, Lightweight Directory Access Protocol (LDAP) or SSO, an SLA, installation support, and team onboarding.

Kubeflow: Best for model deployment/serving

Kubeflow is an open-source platform for deploying and serving ML models. Google created it as the machine learning toolkit for Kubernetes, and it is currently maintained by the Kubeflow community.

Key Features

• Easy model deployment: Kubeflow makes it easy to deploy your models in various formats, including Jupyter notebooks, Docker images, and TensorFlow models. You can deploy them on your local machine, in a cloud provider, or on a Kubernetes cluster.
• Seamless integration with Kubernetes: Kubeflow integrates with Kubernetes to provide an end-to-end ML solution. You can use Kubernetes to manage your resources, deploy your models, and track your training jobs.
• Flexible architecture: Kubeflow is designed to be flexible and scalable. You can use it with various programming languages, data processing frameworks, and cloud providers such as AWS, Azure, Google Cloud, Canonical, IBM cloud, and many more.

Pros

• Easy to install and use
• Supports a variety of programming languages
• Integrates well with Kubernetes at the back end
• Flexible and scalable architecture
• Follows the best practices of MLOps and containerization
• Easy to automate a workflow once it is properly defined
• Good Python SDK to design pipeline
• Displays all logs

Cons

• An initial steep learning curve
• Poor documentation

Pricing

Open-source

Databricks Lakehouse: Best end-to-end MLOPs platform

Databricks is a company that offers a platform for data analytics, machine learning, and artificial intelligence. It was founded in 2013 by the creators of Apache Spark. And over 5,000 businesses in more than 100 countries—including Nationwide, Comcast, Condé Nast, H&M, and more than 40% of the Fortune 500—use Databricks for data engineering, machine learning, and analytics.

Databricks Machine Learning, built on an open lake house design, empowers ML teams to prepare and process data while speeding up cross-team collaboration and standardizing the full ML lifecycle from exploration to production.

Key Features

• Collaborative notebooks: Databricks notebooks allow data scientists to share code, results, and insights in a single place. They can be used for data exploration, pre-processing, feature engineering, model building, validation and tuning, and deployment.
• Machine learning runtime: The Databricks runtime is a managed environment for running ML jobs. It provides a reproducible, scalable, and secure environment for training and deploying models.
• Feature Store: The Feature Store is a repository of features used to build ML models. It contains a wide variety of features, including text data, images, time series, and SQL tables. In addition, you can use the Feature Store to create custom features or use predefined features.
• AutoML: AutoML is a feature of the Databricks runtime that automates building ML models. It uses a combination of techniques, including automated feature extraction, model selection, and hyperparameter tuning to build optimized models for performance.
• Managed MLflow: MLflow is an open-source platform for managing the ML lifecycle. It provides a common interface for tracking data, models, and runs as well as APIs and toolkits for deploying and monitoring models.
• Model Registry: The Model Registry is a repository of machine learning models. You can use it to store and share models, track versions, and compare models.
• Repos: Allows engineers to follow Git workflows in Databricks. This enables engineers to take advantage of automated CI/CD (continuous integration and continuous delivery) workflows and code portability.
• Explainable AI: Databricks uses Explainable AI to help detect any biases in the model. This ensures your ML models are understandable, trustworthy, and transparent.

Pros

• A unified approach simplifies the data stack and eliminates the data silos that usually separate and complicate data science, business intelligence, data engineering, analytics, and machine learning. 
• Databricks is built on open source and open standards, which maximizes flexibility.
• The platform integrates well with a variety of services.
• Good community support.
• Frequent release of new features.
• User-friendly user interface.

Cons

• Some improvements are needed in the documentation, for example, using MLflow within existing codebases.

Pricing

Databricks offers a 14-day full trial if using your own cloud. There is also the option of a lightweight trial hosted by Databricks.

Pricing is based on compute usage and varies based on your cloud service provider and Geographic region.

Getting Started with MLOPS

MLOps is the future of machine learning, and it brings a host of benefits to organizations looking to deliver high-quality models continuously. It also offers many other benefits to organizations, including improved collaboration between data scientists and developers, faster time-to-market for new models, and increased model accuracy. If you’re looking to get started with MLOps, the tools above are a good place to start.

Also read: Best Machine Learning Software in 2022

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This new tech in AI determines the original pattern entered in the input to generate creative, authentic pieces that showcase the training data features. The MIT Technology Review stated Generate AI is a promising advancement in artificial intelligence.

Generative AI offers better quality results through self-learning from all datasets. It also reduces the challenges linked with a particular project, trains ML (machine learning) algorithms to avoid partiality, and allows bots to understand abstract concepts.

Gartner mentioned Generative AI in its lists of major trends of 2022 and highlighted that enterprises could use this innovative technology in two ways:

• Enhancing current innovative workflows together with humans: Developing artifacts to aid better creative tasks performed by humans. For instance, game designers can utilize generative AI to create dungeons, highlighting what they prefer and don’t prefer about the content created in terms like “somewhat like this” or “little less like that.”
• Functioning as an artifact production unit: Generative AI can produce creative pieces in any quantity with little human involvement (apart from shaping the parameters of what they want to create). It only requires setting the context, and the results will be generated independently.

Benefits of Generative AI

• Protection of your identity: The avatars produced by generative AI offer security to those who don’t wish to reveal their identities during interview sessions or work.
• Robotics control: Generative AI strengthens ML models, makes them less partial, and realizes more abstract concepts in imitating the real world.
• Healthcare: The technology has simple and easy detection of probable malice and develops efficient treatments against it. For instance, Generative Adversarial Networks (GANs) can calculate several angles of an X-ray picture to show the possibility of tumor expansion.

Also read: What’s Next for Ethical AI?

Challenges of Generative AI

• Safety: It has been observed that malicious people use generative AI for scamming purposes.
• Highly estimated abilities: Generative AI algorithms need considerable training data to perform tasks like creating art; however, the images created are not wholly new. Instead, these models only mix and match what they know in the best possible ways.
• Unpredictable outcomes: In some models of generative AI, it is simple to handle their behavior, but sometimes, they may yield erroneous or unexpected results.
• Data Security: With the technology relying on data, sectors like healthcare and defense may face privacy concerns when leveraging generative AI applications.

Is Generative AI Just Supervised Training?

Generative AI is a semi-supervised training framework. This learning methodology involves manually marked training information for supervised training and unmarked data for unsupervised training methods. Here, unmarked data is used to develop models that can predict more than the marked training by enhancing the data quality.

Some of the key advantages of GANs, a semi-supervised framework of generative AI against supervised learning, are:

• Overfitting: Generative AI models have lesser parameters, so it may be tougher to overfit. Also, generative models need a high quantity of data because of the training procedure, making them sturdier to obstructions.
• Human partiality: Human marks are not as evident as in the supervised learning methodology in generative AI modeling. The learning works on the data properties that permit the exclusion of bogus correlations.
• Model partiality: Generative models don’t generate results the same as the training data. Hence, the shape and texture problem disappears.

Also read: What Does Explainable AI Mean for Your Business?

Applications of Generative AI

AI-generative NFTs

With sales of non-fungible tokens (NFTs) reaching $25 billion in 2021, the sector is currently one of the most lucrative markets in the crypto world. Art NFT, in particular, is creating a major impact.

While the most popular art NFTs are cartoons and memes, a new kind of NFT trend is emerging that leverages the power of AI and human imagination. Coined as AI-Generative Art, these non-fungible tokens use GANs to produce machine-based art images.

Art AI is one such example of an art gallery that showcases AI-generated paintings. It released a tool that transforms text into art and helps the creators sell their art pieces on NFT. Metascapes, on the other hand, combine images to generate a new photograph. It uses two learning models, and the output gets better every time. These art pieces are placed on sale online.

Identity security

Generative AI allows people to maintain privacy using avatars instead of images. In addition, it can also help companies opt for impartial recruitment practices and research to present unbiased results.

Image processing

AI is used in extraordinary ways to process low-resolution images and develop more precise, clearer, and detailed pictures. For example, Google published a blog post to let the world know they have created two models to turn low-resolution images into high-resolution images.

The upscale examples include photography of a woman from 64 x 64 input to 1024 x 1024 output. The process helps restore old images and movies and upscale them to 4K and more. It also helps to transform black and white movies into color.

Healthcare

Generative AI better identifies an ailment to help patients receive impactful treatment even during the early stages.

Audio synthesis

With Generative AI, it is possible to create voices that resemble humans. The computer-generated voice is helpful to develop video voiceovers, audible clips, and narrations for companies and individuals.

Design

Many businesses now use generative AI to create more advanced designs. For instance, Jacobs, an engineering company, used generative design algorithms to design a life-support backpack for NASA’s new spacesuits.

Client segmentation

AI allows users to acknowledge and differentiate target groups for promotional campaigns. It learns from the available data to estimate the response of a target group to advertisements and marketing campaigns.

Generative AI also helps develop customer relationships using data and gives marketing teams the power to enhance their upselling or cross-selling strategies.

Sentiment analysis

ML involves using text, pictures, and voice evaluation to grasp people’s emotions. For example, AI algorithms can learn from web activity and user data to interpret customers’ opinions towards a company and its products or services.

Detecting fraud

Several businesses already use automated fraud-detection practices that leverage the power of AI. These practices have helped them locate malicious and suspicious actions quickly and with superior accuracy. AI is now detecting illegal transactions through preset algorithms and rules and is making the detection of theft identification easier.

Trend evaluation

ML and artificial learning technology are helpful to predict the future. These technologies aid in providing valuable insights on the trends beyond conventional calculative analysis.

Software development

Generative AI has also influenced the software development sector by automating manual coding. Rather than coding the software completely, the IT professionals now have the flexibility to quickly develop a solution by explaining the AI model about what they are looking for.

For instance, a model-based tool GENIO can enhance a developer’s productivity multifold compared to a manual coder. The tool helps citizen developers, or non-coders, develop applications specific to their requirements and business processes and reduces their dependency on the IT department.

The Road Ahead for Generative AI Looks Promising

While generative AI is becoming a boon today for image production, restoration of movies, and 3D environment creation, the technology will soon have a significant impact on several other industry verticals. By empowering machines to do more than just replace manual labor and take on creative tasks, we will likely see a broader range of use cases and adoption of generative AI across different sectors.

Read next: Top Artificial Intelligence (AI) Software 2022

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What is AI Software?

An AI software can imitate or surpass human intelligence with no physical intervention. AI software achieves levels of intelligence by learning numerous patterns of data and insights that are continually being adjusted through algorithm training, thereby building more intelligent software.

AI software features include machine learning (ML), business intelligence (BI), virtual assistance, and speech and voice recognition.

The benefits of AI tools include:

• Error reduction
• Time management
• Repetitive task management
• 24/7 availability

What are the Types of AI Software?

There are three types of AI software:

• Robot process automation (RPA): RPA is the configuration and automation of tasks such as administrative processes, addressing queries, transactions, and financial activities using AI and ML. 
• Cognitive insight: Cognitive insight refers to the use of deep learning to help organizations detect relevant patterns in large volumes of data and interpret their meaning to predict outcomes. 
• Cognitive engagement: Cognitive engagement utilizes natural language processing (NLP) and ML to help organizations create personalized customer strategies and engage efficiently.

Also read: Top 8 AI and ML Trends to Watch in 2022

Top AI Software

Automation Anywhere Automation 360

Automation 360 by Automation Anywhere is an end-to-end, cloud-native, intelligent RPA platform that enables you to automate tasks across all systems and applications, including legacy and software-as-a-service (SaaS) applications.

With Automation 360, you can automate several tasks immediately from the start; scale rapidly; and improve security, agility and innovation at a low cost.

Key Differentiators

• Discovery Bot enables you to unravel and document the highest return on investment (ROI) automation opportunities.
• With the Private Bot Store, you can crowdsource your best bot ideas and best practices. 
• IQ Bot uses AI and ML to convert structured and unstructured data into usable digital assets. 
• Bot Insight enables you to take critical insights on every task and have an error-free pulse on every bot in real time. 
• You can securely automate repetitive processes with RPA Workspace. By integrating automation into employees’ day-to-day functioning, you can enhance their working experience.
• Front office automation helps raise your customer satisfaction score. You can optimize time spent with customers and efficiently resolve issues.
• Back office automation helps convert complicated manual processes into streamlined automations. This reduces human error and enhances digital transformation.

Pricing: You can try a 30-day free trial today. The software provider offers students and developers free access to the Community Edition of Automation 360. For pricing details, contact the Automation Anywhere sales team.

Microsoft Power Automate

Power Automate by Microsoft is a robust RPA tool that enables you to streamline tedious, repetitive processes and paperless tasks. The platform utilizes AI to automate processes securely and rapidly, enhance workflows, and boost overall efficiency.

Key Differentiators

• The AI software is available for desktop, mobile, web, and Microsoft Teams.
• Utilize low-code/no-code, drag-and-drop tools, and a multitude of prebuilt connectors (such as Dropbox, OneDrive, and Google Calendar) to build automated processes. 
• With Process Advisor, you can apprehend and record end-to-end processes—Process Advisor provides deep insights and guided recommendations for creating flows.
• AI Builder helps make your automation more intelligent. You can rapidly process forms using detect images and text, process approvals, and document automation or design with prebuilt models.
• With thousands of prebuilt templates to choose from, you can automate a variety of business processes.
• You can extend the capabilities of Power Automate by connecting the platform to applications like Azure and Microsoft 365.

Pricing: You can explore Power Automate for free today. Power Automate starts at $15 per user, per month 

Splunk Enterprise

Splunk Enterprise is a predictive analytics tool that enables you to turn data into answers with intuitive, ML-powered analytics. With the solution, you can harness the untapped value of data and optimize the workflow of your organization.

Key Differentiators

• You can automate the gathering, indexing, and alerting of data that is important to your operations for real-time visibility. 
• The AI tool is data source agnostic—you can ingest data from multiple sources, such as interactions, devices, and systems, and unravel actionable insights.
• You can leverage AI and ML for proactive and predictive business decisions and improved IT and security. 
• Customizable tools and integrated technologies grant you access to algorithms, so you can introduce more intelligence to data.
• Uncover the power of visual metrics to get speedy answers—boost monitoring and search performance and convert logs into metrics. 
• With Splunk Operator for Kubernetes, you can deploy, manage, and scale Splunk Enterprise on your choice of cloud.

Pricing: Reach out to the Splunk team for pricing information.  

LOVO Studio

LOVO Studio enables you to create high-quality, AI voice-overs. You can use the free version of the AI tool for unlimited conversion, listening, and sharing but with limited monthly downloads and access to premium voices.

Key Differentiators

• LOVO Studio is fairly easy to use. The AI software offers three steps to create a voice-over for any content type—type, convert, and play. 
• You can choose from a library of over 180 voices and 30 languages to find the best fit for your content type and tone. 
• To create a voice-over, enter your script into their Workspace either by uploading a file or typing. 
• You can make a voice-over sound more natural by changing the tempo, adding emphasis to words, and playing around with pauses. 
• With the AI software, you can add background music to voice-overs, obtain commercial rights, and download up to 100 files per month. 
• You can integrate the text-to-speech (TTS) technology with your own application or product through LOVO API, which is built around REST.
• By integrating LOVO API (sold separately) with your call center software, you can automate the process of creating, downloading, and uploading a voice-over to your native environment or a third-party OHM/VR platform.

Pricing: LOVO Studio is available for free but with limited features. The paid version of the AI software starts at $17.49 per month (Personal), paid annually. LOVO Studio Freelancer is available for $49.99 per month, paid annually.

LOVO API starts at $45 per month, per 1,000 calls. If you are expecting over 1 million calls monthly or require chatbots, you will have to purchase the Enterprise License.

Also read: Best Machine Learning Software in 2021

Google Cloud

Google Cloud is an all-in-one platform that helps accelerate your digital transformation by aiding in quick application building and intelligent decision-making. The solution offers AI and ML solutions, smart analytics and a business application platform.

Key Differentiators

• An open, flexible, and multicloud platform enables you to maximize value from data. 
• With streaming analytics and real-time intelligence, you can optimize business outcomes. 
• Marketing analytics provide a comprehensive view of the customer journey and help predict outcomes and create customized customer experiences.
• With prebuilt datasets, you can enhance analytics or AI initiatives.
• By extending existing data with APIs, you can create applications without coding and securely automate processes.    
• Contact Center AI provides improved operational efficiency and individualized customer care. The AI solution’s features include speech-to-text, text-to-speech, NLP, and Dialogflow. 
• With Document AI, you can make sense of unstructured data to enhance customer experience and operational efficiency.

Pricing: Google Cloud offers a pay-as-you-go pricing plan. You can get started for free or request a quote. All customers can use over 20 products for free, up to their monthly usage bounds.   

Choosing AI Software

The artificial intelligence software analyzed in this guide is among the best tools available today. It is necessary to find ways to reduce human effort in the software development process, and AI tools offer just that—they make the life of software developers much easier. Dive further into the specifics of the AI tools mentioned in this guide and do your own research.

You should visit the website of each solution, analyze product features, explore pricing plans, and scrutinize peer reviews. Purchase a tool of your choice upon arriving at a well-researched conclusion.

Read next: 2022 AIOps Forecast: Trends and Evolutions

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“I like to think about AI CX with ‘A’ standing for ‘Access’ and ‘I’ standing for ‘Intimacy,’” said Puneet Mehta, CEO of Netomi. “AI CX enables this combination of access and intimacy between businesses and their customers in a very goal-driven way.”

It’s about achieving true one-on-one interactions that scale.

“AI CX replaces bland, generic customer experiences of yesteryear,” said Jaime Meritt, chief product officer at Verint.

So, let’s take a deeper look at AI CX with a focus on its capabilities, use cases, and downsides.

The Importance of CX

CX is becoming a key differentiation and competitive advantage. Examples of this include Amazon, Facebook, Apple, and Uber. They have disrupted major industries and built high-growth businesses. 

But it is difficult to develop a strong CX platform, partly due to the reliance on a myriad of legacy systems that spread data across silos. The result is that it is challenging to get a holistic view of the customer journey. Companies also do not effectively analyze the data, such as for intent, sentiment, and emotion. For the most part, the approach is more of a guessing game.

In the meantime, there are continuing labor shortages. This means it is difficult to hire and retain support agents to provide better service. 

“Everyday, revenue teams are responsible for maintaining communications with large volumes of current and prospective customers,” said Erica Hansen, VP of customer success at Conversica. “Each of these contacts expects a highly personalized experience that is frankly impossible to deliver when each human representative is trying to juggle 50 or even 100 customers

“In particular, customer account managers often end up prioritizing a few select high-value accounts while giving less attention to smaller, less invested customers.”

Also read: MetaCX Brings Customer Experience Management to the Fore

CX Powered by AI 

Whenever it comes to solving a tough problem, it seems that AI is the reflex answer. However, artificial intelligence requires huge amounts of quality data as well as algorithms that are relevant, so it may not always be the best choice. But in regards to CX, artificial intelligence does make a lot of sense. 

“AI CX is the perfect pairing of artificial intelligence and human intelligence meeting to satisfy the human customer,” said Muddu Sudhakar, CEO of Aisera.

Yet, there are still challenges. Before an implementation, there needs to be a well-thought out plan that’s based on clear key performance indicators (KPIs). There also needs to be a unified environment where data is consolidated and easily integrated across customer touchpoints. It shouldn’t matter what channel a customer is using; the main focus is on getting the right insights. 

Moreover, don’t try for a big-bang strategy. That is, you should focus on a particular use case. And yes, a good place to start is with the customer service department.

“So much customer service friction stems from the time spent creating identity, such as ‘who am I talking to?,’ and establishing intent, such as with ‘what can I help you with?’” said Shawna Wolverton, EVP of product at Zendesk. “This is where AI can seamlessly handle the dance between the company and their customer.

“Instead of people feeling the frustration of repeating themselves multiple times to multiple different agents, they have a better—and faster—experience.”

The customer service department has a large number of tickets that AI models can process. The tickets also involve repetitive questions and issues. 

“AI CX is much more advanced than rigid rules-based chatbots that rely on buttons and keywords,” said Mehta. “It needs to be able to decipher the intent of a person’s message using natural language understanding.

“In one example, Comcast found that there are 1,700 ways a person might ask one straightforward question: ‘I want to pay my bill.’ Without training data, an AI might not be able to initially understand that ‘I’d like to settle my account’ has the same meaning.”

Regardless of the sophistication of the AI, there still needs to be a seamless off-ramp to a human agent. But the heavy-lifting of the technology should allow for more time spent on value-add activities. AI can also help the human agent with recommendations on how to best handle a situation.

Another way to improve CX is to be more proactive. 

“AI can surface problems that need to be fixed before customers are aware of an issue and can predict and prevent future issues,” said Michael Ramsey, VP of product management and customer workflows at ServiceNow. “This drives up customer satisfaction.”

There are certainly downsides to AI CX. Understanding human interactions is extremely complex, and misunderstandings can cause dissatisfaction with customers. Other downsides can include biased data, which could lead to discrimination and unfair treatment, and a myriad of privacy laws and regulations that will need to be considered before making any implementations.

The Ongoing AI/CX Relationship

While there are notable risks to AI CX, the benefits of the technology are too important to ignore. They can make a significant difference in boosting growth. The key is to continue to improve on the systems and focus on responsible AI. 

“AI empowers organizations to deliver better customer experiences because it enables them to understand customer needs and context and to do it faster than ever before,” said Tara DeZao, product marketing director for AdTech and MarTech at Pega. “Today’s organizations have a near-infinite amount of customer data at their fingertips, but that means nothing if they’re not using that data to inform their customer interactions at the exact time they are. AI makes that process more accurate, efficient, informed, and most importantly, more customer-centric.”

Read next: 6 Ways Your Business Can Benefit from DataOps

The post AI CX (Customer Experience): What You Need to Know appeared first on IT Business Edge.

AI/ML technologies come with their share of challenges. Globally leading brands such as Amazon, Apple, Google, and Facebook have been accused of bias in their AI algorithms. For instance, when Apple introduced Apple Card, its users noticed that women were offered smaller lines of credit than men. This bias seriously affected the global reputation of Apple.

In an extreme case with serious repercussions, U.S. judicial systems use AI algorithms to declare prison sentences and parole terms. Unfortunately, these AI systems are built on historically biased crime data, amplifying and perpetuating embedded biases in AI systems. Ultimately, this leads to questioning the fairness offered by the ML algorithms in the criminal justice system.

The Fight for Ethical AI

Governments and corporations have been aggressively getting into AI development and adoption globally. Today, the availability of AI tools that even non-specialists can set up has been increasingly entering the market.

Amid this AI adoption and development spree, many experts and advocates worldwide have become skeptical about AI applications’ long-term impact and implications. They are concerned about how AI advancements will affect our productivity and the exercise of free will; in short, what it means to be “human.” The fight for ethical AI is nothing but fighting for a future in which technology can be used not to oppress but to uplift humans.

Global technology behemoths such as Google and IBM have researched and addressed these biases in their AI/ML algorithms. One of the solutions is to create documentation for the data used to train AI/ML systems.

After the issue of biases in AI systems, another most widely publicized concern is the lack of visibility over how AI algorithms arrive at a decision. It is also known as opaque algorithms or black box systems. The development of explainable AI mitigated the adverse impact of black box systems. While we have overcome some ethical AI challenges, several other issues like the weaponization of AI are yet to be solved.  

There are many governmental, non-profit, and corporate organizations concerned with AI ethics and policy. For example, the Partnership on AI to Benefit People and Society, a non-profit organization established by Amazon, Google, Facebook, IBM, and Microsoft, formulates best practices on AI technologies, advances the public’s understanding, and serves as a platform for AI. Apple joined this organization in January 2017.

Today, there are many efforts by national and transnational governments and non-government organizations to ensure AI ethics.In the United States, for example, the Obama administration’s Roadmap for AI Policy of 2016 was a significant leap towards ethical AI, and in January 2020, the Trump Administration released a draft executive order on “Guidance for Regulation of Artificial Intelligence Applications.” The declaration emphasizes the need to invest in AI system development, boost public trust in AI, eliminate barriers to AI, and keep American AI technology competitive in the international market.

Moreover, the European Commission’s High-Level Expert Group on Artificial Intelligence published “Ethics Guidelines for Trustworthy Artificial Intelligence,” on April 8, 2019, and on February 19, 2020, the Robotics and Artificial Intelligence Innovation and Excellence unit of The European Commission published a white paper on excellence and trust in artificial intelligence innovation.

On the academic front, the University of Oxford accommodates three research institutes that focus mainly on AI ethics and promote AI ethics as a structured field of study and applications. The AI Now Institute at New York University (NYU) also researches the social implications of AI, focusing on bias and inclusion, labor and automation, liberties and rights, and civil infrastructure and safety.

Also read: ​​AI Suffers from Bias—But It Doesn’t Have To

Some Key Worries and Hopes on Ethical AI Development

Worries

• The major AI/ML system developers and deployers are focused on profit-making and social control. There is still no consensus about what ethical Al would look like. Many experts worry that ethical AI behaviors and outcomes are hard to define, implement and enforce.
• Powerful technology companies and governments control Al’s development, and their agendas rather than ethical concerns drive them. It has been speculated that they will use Al/ML technologies to create more sophisticated methods to exert influence over human psychology to convince us to buy goods, services, and ideas over the next decade.
• The operation of AI tools and applications in black box systems is still a concern. Besides, applying ethical AI standards under these opaque conditions remains an unanswered question.
• The technology arms race between China and the U.S. will do more to the development of Al than to the ethical AI issues. Plus, these two superpowers define ethics in different ways. Ethics always takes a back seat when it comes to acquiring power.

Hopes

• AI/ML development has clearly shown its progress and value. However, human societies have always found ways to mitigate the issues from this technological evolution so far.
• AI tools and applications have been doing amazing things beyond human capabilities to date. Further innovations and breakthroughs will only add to this.
• The limitless rollout of new Al systems is inevitable. Along with that, developing AI strategies that can mitigate harm is also inevitable. Indeed, we can utilize ethical Al systems to identify and rectify issues arising from unethical AI systems.
• In recent years, the global initiatives on ethical Al have been productive. It moves human societies toward adapting to further AI development based on mutual benefits, safety, autonomy, and justice.
• Imagine a future where even more AI tools and applications emerge to make our lives easier and safer. Al will radically enhance every human system from healthcare to travel; therefore, it is likely that support for ethical Al will substantially grow in the coming years.
• A consensus has been building around ethical Al, particularly in the biomedical community, with the help of open-source technology. For several years, extensive study and discourse in this vital area of ethical Al have been bearing fruit.
• No technology survives if it broadly delivers harmful and futile results. The market and legal systems will eventually kick out unethical Al systems.

Also read: Using Responsible AI to Push Digital Transformation

The Responsibility of Ethical AI

Tech giants like Microsoft and Google think governments should step in to regulate AI effectively. Laws are only as good as how they are enforced. So far, that responsibility has fallen onto the shoulders of private watchdogs and employees of tech companies who are daring enough to speak up. For instance, after months of protests by its employees, Google terminated its Project Maven, a military drone AI project.

We can choose the role we want AI to play in our lives and enterprises by asking tough questions and taking stern precautionary measures. As a result, many companies appoint AI ethicists to guide them through this new terrain.

We have a long way to go before artificial intelligence becomes one with ethics. But, until that day, we must self-police how we use AI technology.

Read next: Top 8 AI and ML Trends to Watch in 2022

The post What’s Next for Ethical AI? appeared first on IT Business Edge.

As businesses worldwide have embraced digitization and virtualization, another trending buzzword emerged: AIOps. Artificial intelligence for IT operations, or AIOps, is the technology that converges big data and ML. This latest technology seamlessly automates enterprise IT operation processes, including event correlation, anomaly detection, and causality determination.

The following are six key trends and evolutions that can shape AIOps in 2022.

1. The C-Suite’s Increased Interest in AIOps

The International Data Corporation (IDC) forecasts that global enterprise spending on AI frameworks is expected to double in the next four years, reaching approximately $110 billion in 2024.

The use cases of AI have been increasing exponentially in the business world. But what caught the eye of the C-suite is the ease with which AI can handle IT operations as they are going through an advanced transformation phase. The C-suite of businesses worldwide is getting ready to tap into both the short-term and long-term advantages of AIOps. They are aware that the implementation of AIOps can simplify their administration tasks, DevOps, and information security (InfoSec). Along with that, as AIOps tools evolve for the better, they will be able to process a wider variety of data types. As a result, it can lead to speedy and accurate delivery of business value and enhance the performance of more specific enterprise operations.

2.The Expansion of Incident Management Capacities

AIOps will be extensively utilized to enhance natural language processing (NLP), cause analysis, event correlation, and anomaly detection among various IT functions in the coming years. In this way, it offers more control to IT tasks experts. These IT tasks, particularly event correlations and incident intelligence, can be consistently accessible inside a functional IT team’s optimal incident management platform. In addition, irregularities in the system can be proactively identified and rectified.

3. An Extensive Automation in a More Intelligent Way

The significant advantage of AIOps is its ability to automate IT operations. To this day, the automation ability of AIOps has been operating in a limited space. AIOps tools could handle only a single type of solitary information at a time. But, today, the emergence of new AI algorithms can deal with numerous data types at double the processing power.

Today, only a few enterprises use robotic data automation (RDA) and AIOps to deal with data issues. However, the adoption rate of RDA and AIOps will increase in the coming years, drastically diminishing the requirement for human intervention in enterprise operations. A recent study reveals that the global AIOps market will surpass $3 billion by 2025 at a CAGR of 43.7% from 2020 to 2025. All of this research and observations leads many to anticipate that most AIOps tools will turn into more innovative and developed devices in 2022.

4. Enhancing Cybersecurity

Today, businesses worldwide rely more on IT operations to smoothly run their business and boost performance. Indeed, despite all the innovations and progress in IT operations, cybersecurity stands as the primary concern among business organizations. Moreover, the need for cutting-edge cybersecurity systems increases when more enterprises begin to transform themselves digitally.

AIOps seems to have more scope in 2022 as it incorporates high-end security features. Utilizing AIOps allows an enterprise to identify security issues and proactively deploy preventative measures immediately. Similarly, it can increase uptime and eliminate the downtime of enterprise IT systems. For instance, AIOps can help an enterprise distinguish between reliable access and unreliable access from users. Once differentiated, it can automatically block the entry of any suspicious user into the system.

Also read: The Pros and Cons of Enlisting AI for Cybersecurity

5. AIOps Will Merge With DevOps

Traditional IT tools cannot deal efficiently with the volume, velocity, and variety (the three Vs) of big data. The advent of advanced analytics tools, AI algorithms, and deep learning models helps DevOps professionals to handle data effectively. In addition, AIOps allows rapid data processing, performs deep data analysis, and automates routine IT tasks.

In test monitoring and management, performance, and security, AIOps assists DevOps engineers to a greater extent. Moreover, AIOps comes with secured solutions to complex IT infrastructure management and monitoring tools of cloud solutions. It can easily automate routine DevOps operations and data analysis. In short, the convergence of AIOps and DevOps is something worth watching in 2022.

Also read: Top DevOps Trends to Watch in 2022

6. The Widespread Adoption of 5G Technology

In 2022, the rate of 5G adoption is widely expected to outpace 4G technology. In its November 2020 report, Ericsson predicts that 5G technology will surpass three billion subscriptions by 2026, turning it into the fastest mobile network generation ever to be rolled out on a global scale.

Regarding AIOps, 5G technology will lay a solid foundation for an intelligent, connected environment. It is not due to its superior speed over 4G technology but its reliability and low latency. Several leading global companies have already begun rolling out 5G-enabled Internet of Things (IoT) devices like smartphones and biometric devices.

These technological developments will lead to more data generation and transfer at higher speeds. Moreover, it is doubtless that 5G will reshape the technology and mobility landscape over the next decade. Therefore, the demand for technology like AIOps will rise in the coming days whether enterprises ride the 5G wave or not.

There is great excitement around the 5G rollout on a global scale. From high-speed mobile networks to remote healthcare and education, the possibilities of 5G are limitless. It’s the right time to embrace AIOps to uncover the business benefits from adding value for your customers.

Also read: 5G Cybersecurity Risks and How to Address Them

AIOps’ Future in Enterprises

The current global business ecosystem is fast-paced, and humans alone cannot keep up with this pace. AIOps optimizes IT processes and, in turn, aids in reducing manual effort and smooth running of business processes.  

AIOps is a game-changer for enterprises that are slow to adapt. The strategic relevance and visibility of enterprise IT is growing alongside the need for optimal performance and continuous availability. Currently, AIOps is challenging the IT segment of enterprises worldwide. This trend will continue in the coming years.

Today, instead of sustaining legacy systems, enterprises have begun utilizing AIOps to eliminate problems, save costs, improve customer relationships, and divert the workforce to focus on developing cutting-edge technological solutions.

A brighter future awaits AIOps. As technology becomes more user-friendly, it will unlock the potential of big data and significantly reduce business expenses. The sooner your enterprise begins to utilize AIOps, the better it is for the future of your business and its IT operations.

Read next: Top 8 AI and ML Trends to Watch in 2022

The post 2022 AIOps Forecast: Trends and Evolutions appeared first on IT Business Edge.

NLP itself is an umbrella term that refers to a bunch of related technologies. NLP is at the core of Sentiment analysis, text extraction, machine translation, conversational AI, document AI, text summarization … and the list goes one. As AI systems become more and more intelligent, these systems would need to interact with humans in a rich, context-aware manner. It is NLP that would make it possible for machines to understand the context in which they operate. For example, when a user says ‘bank’ in the context of a financial institution, NLP engines can differentiate it from a river ‘bank’ and so on. This higher level of intelligence is a primary requirement for humans to converse with machines smoothly.

The Technology Drivers Behind NLP’s Success

Traditionally, NLP has been a complex problem to solve. However, two significant advances—one in 2017 and another in 2019—brought substantial improvements to NLP. In 2017, a new form of deep learning model called Transformer made it possible to parallelize ML training more efficiently, resulting in vastly improved accuracies. 

In 2019, Google introduced Bidirectional Encoder Representations from Transformers (BERT), which improves the above Transformer architecture. Straightaway BERT helped achieve state-of-the-art performance on several NLP tasks such as reading comprehension, text extraction, sentiment analysis, etc. These two advancements meant that NLP could easily outdo average humans in many tasks and in some cases, even exceed the performance of subject matter experts.

Also read: Natural Language Processing Will Make Business Intelligence Apps More Accessible

So, How Big is the NLP Market?

The NLP market is at a relatively nascent stage but is fast expanding. According to the research firm, MarketsandMarkets, the NLP market would grow at a CAGR of 20.3% (from 11.6 billion in 2020 to USD 35.1 billion by 2026). Research firm Statistica is even more optimistic. According to their October 2021 article, NLP would catapult 14-fold between the years 2017 and 2025. This is certainly a phenomenal growth for a technology that was pretty much confined to the labs even as late as a decade ago. 

A Word of Caution

Even as the NLP market grows and becomes mainstream, practitioners should be careful while investing in NLP. First and foremost is the understanding that NLP is not a single technology, but a suite of technologies. Consequently, not all the underlying systems have the same maturity curve. In principle, practitioners should value NLP along two dimensions—one that measures business benefits and two, the propensity of the underlying NLP technology to become mainstream.  

According to Gartner, technologies such as conversational AI, chatbots, and document AI are expected to bring high to very high (transformational) business benefits while promising to become mainstream in less than two years. Contrast this with technologies such as text summarization which, according to Gartner, will likely bring in moderate benefits and will take 5-10 years to mature! Thus it is clear that not all the underlying NLP technologies are born equal, and investments require careful scrutiny.

Another important consideration for practitioners is the choice of natural language. Most models are good at English language followed by Chinese while performing below par on several international languages. Similarly, these language models tend to show a cultural and regional bias as many of them are trained on public datasets that have a large exposure to the western world. 

Lastly, the adoption of NLP varies widely between industries with Healthcare (Drug Discovery, Clinical Trial Analytics, EHRs) taking the lion’s share of the NLP usage, followed by paper-heavy industries such as insurance and mortgage. 

Also read: Top Automation Software for Managing IT Processes

The Future of NLP

The roadmap for NLP is dotted by two major trajectories—the first, powered by larger Transformer Models such as GPT-3 and its future cousins. The second significant advancement will be in dialogue models where Google, Facebook, and other companies pour millions of dollars into research and development. First, let us discuss transformer models. 

GPT-3 was developed by Open AI, a research business co-founded by Elon Musk and has several big names such as Sam Altman to its repertoire. GPT-3 is a multitasking system that can do several things such as translate text, extract text, converse with a human, and if you are bored, it can humor you with its poems. However, where GPT-3 has become savvy (and practically useful) is in the field of generating software code. Given basic instructions, GPT-3 can develop complete programs in Python, Java, and several other languages paving the way for exciting future opportunities. The future beckons bigger and bigger transformer models such as GPT-4 or the Chinese version called Wu Dao 2.0 (which is 10 times that of GPT-3).

The second major trend in NLP involves research from Google and Facebook around dialog models and conversational AI. Google, for example, unveiled a demonstration of a conversational AI system called LAMDA. The power of LAMDA is that it can connect with humans on a seemingly endless number of topics, unlike the modern chatbots which are trained for narrow conversations. If successful, LAMDA would very likely disrupt help desk, customer support and as one Google blog puts it, it will usher, “entirely new categories of helpful applications.”  

Promising Advancements

We can argue that recent developments in NLP make it alluring for investments by practitioners and tech aficionados. The NLP market itself is fast-growing with increased adoption in healthcare, finance, and insurance. NLP is a suite of technologies, and practitioners can do well to discern which of the underlying systems will bring the maximum business benefit and by when. The future of NLP is very promising as more advancements would bring better user experience, thus opening up newer markets.  

Interested in Natural Language Processing? Coursera has a Natural Language Processing Specialization course offered by DeepLearning.Ai worth checking out.

Read next: Leveraging Conversational AI to Improve ITOps

The post The Future of Natural Language Processing is Bright appeared first on IT Business Edge.

Now, it may be difficult to imagine the impact of machines making faster and more accurate decisions than humans, but one thing is certain: In 2022, new trends and breakthroughs will continue to emerge and push the boundaries of AI and ML.

Here are the top eight AI and ML trends to watch out for in 2022.

1. An Efficient Workforce

Since the advent of AI and ML, there have always been fears and concerns regarding these disruptive technologies that will replace human workers and even make some jobs obsolete. However, as businesses began to incorporate these technologies and to bring AI/ML literacy within their teams, they noticed that working alongside machines with smarter cognitive functionality, in fact, boosted employees’ abilities and skills.

For instance, in marketing, businesses are already using AI/ML tools to help them zero in on potential leads and the business value they can expect from potential customers. Furthermore, in engineering, AI and ML tools allow predictive maintenance, an ability to predict and inform the service and repair requirements of enterprise equipment. Moreover, AI/ML technology is widely used in fields of knowledge, such as law, to peruse ever-increasing amounts of data and find the right information for a specific task.

2. Natural Language Processing (NLP)

NLP is currently one of the most used AI technologies. This technology significantly reduces the necessity for typing or interacting with a screen as machines began to comprehend human languages, and we can now simply talk with them. In addition, AI-powered devices can now turn natural human languages into computer codes that can run applications and programs.

The release of GPT-3—the most advanced and largest NLP model ever created—by OpenAI is a big step in language processing. It consists of around 175 billion ‘parameters’—data points and variables that machines use for language processing. Now, OpenAI is developing GPT-4, a more powerful successor of GPT-3. Speculations reveal that GPT-4 may contain roughly 100 trillion parameters, making it 500 times larger than GPT-3. This development is literally a bigger step closer to creating machines that can develop language and engage in conversations that are indistinguishable from those of a human.

Some of the NLP technologies expected to grow in popularity are sentiment analysis, process description, machine translation, automatic video caption creation, and chatbots.

Check out this course to learn more about NLP!

Also read: Natural Language Processing Will Make Business Intelligence Apps More Accessible

3. Enhanced Cybersecurity

Recently, the World Economic Forum stated that cybercrime poses a more significant threat to society than terrorism. When more intelligent yet complex machines connected to a vast network take control of every aspect of our lives, cybercrimes become rampant, and cybersecurity solutions become complex.

AI and ML tools can play a significant role in tackling this issue. For example, AI/ML algorithms can analyze higher network traffic and recognize patterns of nefarious virtual activities. In 2022, some of the most significant AI/ML technology developments are likely to be in this area.

TechRepublic Academy offers great courses on Cybersecurity and are available here.

4. The Metaverse

The metaverse is a virtual world, like the internet, where users can work and play together with immersive experiences. The concept of the metaverse turned into a hot topic since Mark Zuckerberg, the CEO of Facebook, spoke about merging virtual reality (VR) technology with the Facebook platform.

Without a doubt, AI and ML will be a lynchpin of the metaverse. These technologies will allow an enterprise to create a virtual world where its users will feel at home with virtual AI bots. These virtual AI beings will assist users in picking the right products and services or helping users relax and unwind themselves by playing games with them.

Also read: What is the Metaverse and How Do Enterprises Stand to Benefit?

5. Low-code and No-code Technologies

The scarcity of skilled AI developers or engineers stands as a major barrier to adopting AI technology in many companies. No-code and low-code technologies come to the rescue. These solutions aim to offer simple interfaces, in theory, to develop highly complex AI systems.

Today, web design and no-code user interface (UI) tools let users create web pages simply by dragging and dropping graphical elements together. Similarly, no-code AI technology allows developers to create intelligent AI systems by simply merging different ready-made modules and feeding them industrial domain-specific data. Furthermore, NLP, low-code, and no-code technologies will soon enable us to instruct complex machines with our voice or written instructions. These advancements will result in the “democratization” of AI, ML, and data technologies.

6. Hyperautomation

In 2022, with the aid of AI and ML technologies, more businesses will automate multiple yet repetitive processes that involve large volumes of information and data. In the coming years, an increased rate of automation can be seen in various industries using robotic process automation (RPA) and intelligent business process management software (iBPMS). This AI and ML trend allows businesses to bring down their dependence on the human workforce and to improve the accuracy, speed, and reliability of each process.

Also read: The Growing Relevance of Hyperautomation in ITOps

7. Quantum AI

Modern-day businesses will soon begin utilizing AI powered by quantum computing to solve complex business problems faster than traditional AI. Quantum AI offers faster and more accurate data analyzing and pattern prediction. Thus, it assists various businesses in identifying unforeseen challenges and bringing out viable solutions. As a result, quantum AI will revolutionize many industrial sectors, such as healthcare, chemistry, and finance.

To learn more about Quantum Machine Learning, check out this course!

8. The Domain of Creativity

Creativity is widely considered a skill possessed only by humans. But today, we are witnessing the emergence of creativity in machines. That means artificial intelligence is inching closer to real intelligence.

We already know that AI can be used to create art, music, plays, and even video games. In 2022, the arrival of GPT-4 and Google Brain will redefine the boundaries of the possibilities of AI and ML technologies in the domain of creativity. People can now expect more natural creativity from our artificial intelligent machine friends.

Today, most of the creative pursuits of AI technology are rather demonstrations of the potential of AI. But the scenario will significantly change in 2022 as AI technology will get into our day-to-day creative tasks, such as writing and graphic designing.

AI/ML Future Growth

All these trends in AI and ML will soon influence businesses all over the globe. These disruptive technologies are powerful enough to transform every industry by assisting organizations in achieving their business objectives, making important choices, and developing innovative goods and services.

The AI/ML industry is expected to grow at a CAGR of 33% by 2027. Estimates suggest that businesses will have at least 35 AI initiatives in their business operations by 2022.

Data specialists, data analysts, CIOs, and CTOs, should consider using these opportunities to scale their existing business capabilities and use these technologies to the advantage of their businesses.

To learn more about Modern AI with Zero Coding, check out this course!

Read next: Best Machine Learning Software in 2021

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