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Artificial Intelligence is the field of study that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence. These tasks include problem-solving, speech recognition, decision-making, and language translation. AI systems aim to simulate human intelligence and adapt to different situations by learning from experience.

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AI systems require a significant amount of labeled training data to learn from. This data is ingested into the AI system and analyzed for correlations and patterns. By examining the data, the AI system can identify relationships between different variables and extract meaningful insights.

Based on the patterns identified in the data, AI systems can make predictions about future states. For example, a chatbot trained on a vast amount of text data can learn to generate lifelike exchanges with people. Similarly, an image recognition tool can identify and describe objects in images by reviewing millions of examples.

Advancements in machine learning have played a pivotal role in the development of AI. Machine learning algorithms enable computers to process large amounts of data, recognize patterns, and make informed decisions. By leveraging machine learning techniques, AI systems can be trained to accomplish specific tasks.

One of the latest developments in AI is the use of neural networks. These are machine learning models inspired by the structure of the human brain. Neural networks are designed to learn increasingly complex patterns from information. They consist of interconnected layers of artificial neurons that process and analyze data, enabling the AI system to gain a deeper understanding of the underlying patterns.

AI systems have the capability to make real-time decisions, replicating human discernment. Through extensive training and processing of data, AI systems can think, act, and respond just like a real human. This has opened up possibilities for applications such as autonomous vehicles, fraud detection systems, and personalized recommendations.

AI is a broad field with multiple approaches to building intelligent systems. These approaches include symbolic AI, machine learning, evolutionary algorithms, and hybrid models. Symbolic AI focuses on the use of logical rules and representations, while machine learning relies on training algorithms with data. Evolutionary algorithms mimic the process of natural evolution to optimize AI systems. Hybrid models combine different approaches to leverage their respective strengths.

Reactive Machines are the simplest form of AI systems that can only react to specific inputs without any memory or ability to learn from past experiences. These machines analyze the current situation and produce an output based on predefined rules. They do not have the capability to form memories or use past experiences to inform future decisions. Examples of reactive machines include chess-playing computers and voice assistants like Siri or Alexa.

Limited Memory AI systems have the ability to store and recall past experiences to inform future decisions. These systems use historical data to learn and improve their performance over time. One prominent example of limited memory AI is seen in self-driving cars. They store data about road conditions, traffic patterns, and past experiences to recognize and respond to traffic signals, pedestrians, and other vehicles on the road.

Theory of Mind AI is an area of research that aims to develop machines capable of understanding human emotions, beliefs, and intentions. The goal is to create AI systems that can interact with humans in a more natural and intuitive manner. This type of AI would have the ability to perceive and interpret human emotions, allowing for more effective communication and collaboration between humans and machines.

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AI can also be categorized based on its capabilities. The three main categories are:

This type of AI is designed to perform a specific task or a set of tasks within a narrow domain. Examples include voice recognition systems, recommendation algorithms, and image recognition software. Weak AI systems excel in their specific area but lack general intelligence.

General AI refers to machines that possess the ability to understand, learn, and apply knowledge across a wide range of tasks similar to human intelligence. General AI systems would have the capacity to transfer knowledge from one domain to another, demonstrating adaptability and flexibility.

AI systems can also be categorized based on their functionality, which includes:

As mentioned earlier, reactive machines are AI systems that react to specific inputs without memory or learning capabilities.

Limited memory AI systems can store and utilize past experiences to make decisions.

Theory of Mind AI systems aim to understand human emotions, beliefs, and intentions to enhance human-machine interactions.

Artificial Intelligence has revolutionized numerous industries by enabling machines to perform tasks that were once exclusive to humans. By ingesting and analyzing vast amounts of data, AI systems can learn patterns, make predictions, and execute real-time decision making. Advancements in machine learning and neural networks have played a crucial role in enhancing the capabilities of AI systems.

Q1: How does AI learn from data?

AI learns from data by ingesting large amounts of labeled training data and analyzing it for patterns and correlations. This allows the AI system to recognize relationships and make predictions based on the observed patterns.

Q2: Can AI systems adapt to new situations?

Yes, AI systems can adapt to new situations. Through machine learning algorithms, AI systems can learn from new data and adjust their predictions and responses accordingly.

Q3: What are some practical applications of AI?

AI finds applications in various fields such as healthcare, finance, transportation, and customer service. It is used for medical diagnosis, fraud detection, autonomous vehicles, and virtual assistants, to name a few.

Q4: Is AI capable of creativity?

AI systems can exhibit creative behavior by generating novel solutions or artworks. However, the level of creativity is still limited compared to human creativity.

Q5: How can businesses benefit from implementing AI?

Businesses can benefit from implementing AI by automating repetitive tasks, improving decision-making processes, enhancing customer experiences, and increasing operational efficiency.

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Artificial Intelligence Jobs In 2023

Artificial intelligence jobs are not a new phenomenon, but the AI job market is growing as AI market itself is seeing rapid expansion. According to research firm IDC, AI is currently seeing an annual growth rate approach 40 percent.

Amid these changes, AI job titles have changed and expanded – and AI paychecks are heading skyward. Earlier roles were “statistician” or “mathematician,” while today you’ll hear newer terms like “data scientist”  and “predictive analytics expert.”

The rapid emergence of new AI titles reflects the fact that AI has become practical for mainstream use as the result of affordable cloud computing and storage costs, a change from prohibitively expensive supercomputers. The growth of AI companies and the expansion of related technology like machine learning has expanded AI job titles.

Similarly, some of the AI algorithms used today have existed for several decades, but until recently, they lacked the volume and richness of data required to drive value. Now there are many different types of AI jobs or roles available, some of which merely add “AI” to an existing title (e.g., “AI developer”). Others reflect different aspects of AI (e.g., “data engineer,” “algorithm developer” or “machine learning scientist”).

For the purpose of this article, AI job titles, their general job descriptions and their salary ranges are limited to technical titles.

Also see: The Pros and Cons of AI

A typical ad for an AI job in today’s rapid growth artificial intelligence sector.

Jobseekers and career builders are wise not to take AI job titles at face value. For example, some companies require data scientist candidates to hold a PhD or MS in Computer Science or Statistics, while other organizations may accept a BS or even no college degree and certain types of experience. Similarly, a “senior” title may require a graduate degree and more experience at some companies than others.

Some of today’s most popular AI job titles include:

AI developer

AI engineer

Algorithm developer

AWS machine learning engineer

Azure data scientist

Data scientist

Lead data engineer

Lead data scientist

Machine learning scientist

ML data developer

Senior data scientist

Senior ML engineer

It is important to read the mandatory requirements of any job listing to determine whether it is actually a fit. For example, some technical AI positions require R and Python expertise whereas others may only require R or Python experience.

Another reason to maintain an open mind about job descriptions is that the people writing and approving job requisitions may lack the technical expertise to articulate what the organization actually needs. The result is sometimes unrealistic qualifications that even the most sought-after experts lack.

Following are some sample job descriptions that are based on actual job postings. They have been abbreviated for easier comparison.

Job description: Responsible for maintaining, enhancing and implementing AI solutions.

Mandatory skills:

R, Python and/or C#

AWS or Azure AI services and frameworks

AI, ML, NLP, REST APIs, libraries, frameworks

Cloud application design, development and deployment

Hands-on experience with AI, ML, NLP and cloud applications

Education: BS, Computer Science

Job description: Develops solutions to large-scale problems and bridges the gap between software developers and research scientists

Mandatory skills:

ML, DL, NLP, computer vision


Past experience defining and coding and validation tests

Test automation script writing

Programming experience

Education: MS or PhD in Computer Science, AI, ML or related field.

Job description: Develops algorithms for specific use cases.

Mandatory skills:

Algorithm development

Algorithm performance assessment and reporting

Algorithm optimization

Education: BS in Computer Science or equivalent as relevant to the position (e.g., Robotics or Electrical Engineering)

Job description: Build out data models and create very large data sets.

Mandatory skills:

3+ years of AWS or Azure services experience

3+ years ML and data labeling

3+ years Python or R and Python

3 – 5 years data management experience

Education: BS, Compute Science, MS or PhD may be preferred

Job description: Translate customer requirements into POCs and successful solutions.

Mandatory skills:

R and Python


2+ years of data science experience

ML, NLP, DL, depending on the position and clients

Experience with platform (AWS or Azure) and its services

Education: MS in Computer Science or related field

Job description: Organize structured and unstructured data, build data models and interpret complex data sets

Mandatory skills:

R and/or Python

ML and/or NLP, DL depending on the position

Data mining

Education: BS or MS in Computer Science or Equivalent

Job description: Re-engineer business intelligence processes, design and develop data models, and share your expertise throughout the deployment process.

Mandatory skills:

AWS or Azure

Cloud computing

Hadoop, Spark

Python, Scala or Java

ML model deployment

Education: BS, Computer Science or equivalent

Job description: Turn client challenges and goals into successful AI, ML or DL solutions that meet or exceed expectations; train junior data scientists.

Mandatory skills:

R, Python, or both preferred

POC development and demonstration

8+ years end-to-end analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables and presentations

8+ years of development, implementation and use of ML models and quantitative techniques for prediction and classification

2+ years of relevant, specialized experienced (e.g., NLP)

Education: MS in Computer Science, Statistics, or Math; PhD preferred

Mandatory skills:

Proven, in-depth understanding of ML algorithms and modeling including supervised, unsupervised and reinforcement learning models, transfer learning, optimization and probabilistic graphical models

In-depth experience with Spark or Hadoop and either PyTorch or Tensorflow

Python and Java, Scala, and/or R

Experience creating production environment data analytics and applications

Education: PhD in Computer Science or related field with a focus on ML, AI or data mining or an MS plus experience equivalent to holding a PhD

For any number of complex reasons, some AI jobs require additional significant experience, as do the positions below. In truth, the field is still new enough that the exact amount of experience is heavily dependent on individual employers.

Job description: Apply different NLP techniques to areas such as classification, data/knowledge extraction, disambiguation, sentiment analysis, etc. Identify and categorize entities in text such as people, places, organizations, date/time, quantities, percentages, currencies and more.

Mandatory skills:

R, Python and/or C#

AWS or Azure AI services and frameworks (cognitive, bots)

AI, ML, NLP, REST APIs, libraries, frameworks

Cloud application design, development and deployment

Experience Level:

10+ years with the target platform (AWS or Azure)

5+ years development experience

Hands-on POC experience proving an architecture concept

Experience with large, diverse data sets

Education: MS in Computer Science or Computer Information Systems (CIS)

Job description: Responsible for maintaining, enhancing and implementing solutions

Mandatory skills:

R, Python and/or C#

AWS or Azure AI services and frameworks (cognitive, bots)

AI, ML, NLP, REST APIs, libraries, frameworks

Cloud application design, development and deployment

Experience Level:

10+ years software development

7+ years R, Python or C#

5+ years AI, ML, NLP and cloud application development

Education: BS, Computer Science or Engineering

Job description: Design and implement cutting-edge solutions across a breadth of domain areas.

Mandatory skills:

ML, DL, NLP, computer vision, recommendation engines, pattern recognition, large-scale data mining

Predictive, statistical, and data mining modeling

Graph databases

AWS, Azure or IBM Watson

Hadoop, Spark or other Big Data platform

Proficient with ML algorithms

Education: BS or MS in Computer Science, Engineering, Statistics or Math

AI-related jobs tend to share very similar compensation ranges, which may be misleading. While it may seem odd that people with the same title could have base compensation that varies by $50,000 or $100,000, some of the differences have to do with the present levels of compensation in specific markets. For example, jobs in San Francisco and Manhattan tend to pay more than those in other major metropolitan areas given the cost-of-living differences.

Job candidates should therefore go into a job search with an open mind and research what’s available at that point in time. The candidate should also be clear about which titles and compensation packages are acceptable since a better title may not mean more pay or vice versa.

Current salary ranges for the positions discussed above are:

AI developer: $90,000 – $150,000

AI engineer – $130,000 – $210,000

Algorithm developer – $90,000 – $130,000

Machine learning engineer – $70,000 – $170,000

Azure or AWS data scientist – $85,000 – $160,000

Data scientist: $50,000 – $150,000

Lead data engineer: $90,000 – $175,000

Lead data scientist: $125,000 – $195,000

Machine learning scientist: $100,000 – $150,000

ML data developer: $100,000 – $170,000

Senior AI developer: $90,000 – $150,000 (high end)

Senior data engineer: $90,000 – 175,000

Senior data scientist: $95,000 – $210,000

How Does Kubernetes Container Work?

Introduction to Kubernetes Container

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What is a Kubernetes container?

The container in Kubernetes is the software package that has all the information which are needful to run the software like code which is important at runtime, system libraries, and it also has the default values for required settings, in which it needs to have fewer system assets and the applications which are running in the container that can be implemented simply for various operating systems and their hardware platform, every container which is running that can be repeatable it means it follows the standardization from having admiration that means we can get the paired bearing at where we can run it, the container image is a ready-to-run software package having all the information which is needful to run the software including code, libraries and it also needs to have default values for the setting.

How does the Kubernetes container work?

The container is a detachment in the application layer of the Kubernetes, which contains the code and its dependencies together; for sharing the operating system, different containers can run on an identical machine, and every machine can work out of the way in space, the cluster of the Kubernetes can assist the main cloud providers environment so that we can turn the cluster and implement the application by using some commands when Kubernetes can control the bunch of information, the container has a platform is a client-server software can make easier the implementation of the container with its working elements.

A container can start its compilation from the bottom image, and a trial application has been packed into a container image, and that has been implemented via the platform the container; in the computing environment, there are many levels which are starting from the bottom level means bottom level will be the level 1 in which it may be the infrastructure of the container and level 2 having host operating system, level 3 having a platform for container and at the top there are two levels in which one having libraries and next level having application code for services and that are connected with the images and network which is available, this is the working of the container.

Kubernetes container images

We can able to assign the names to the container images for the specification; after that, we can add the tag to the image also consisting of uppercase and lowercase letters, we can also able to update the images.

Kubernetes container environment

The Kubernetes container environment can have some major assets related to the container, the environment of Kubernetes has a filesystem that contains the images and one or more volumes, it also has all the information which are related to the container which is running itself, it also contains the information regarding other devices in the cluster, it has two assets such as container information and cluster information let us discuss them,

1. Container information 2. Cluster information

The cluster information has all the information which are related to the container, and it can be available at the time of generating the container, and that list is restricted to the services under the same namespace when the new pod has been generated and Kubernetes can able to support the plane services.

Kubernetes container runtime

The container runtime is also known as a container engine in which it is an element of software that can allow running the container on a host operating system, for example, docker, runC, containered, and windows containers, etc., it can also accept the requests from user like command-line options, and pull images and it can runs the container in user’s point of view, the container runtime allows to Kubernetes that to utilize the different variety of container without to run the container repeatedly, the Kubernetes can utilize any container runtime which can be deployed CRI for controlling the pods, container and container images.


In this article, we conclude that the Kubernetes container is a software package that has a container image in which it is a software packet containing binary data and its dependencies, container runtime that allows us to run the container on the host operating system; this article will help to understand the concept of Kubernetes container.

Recommended Articles

This is a guide to Kubernetes Container. Here we discuss the Kubernetes container is a software package that has a container image in which it is a software packet. You may also have a look at the following articles to learn more –

How Does Privilege Escalation Work?

Privilege Escalation − What Is It?

WordPress offers a function that lets an administrator grant access to other users to edit the website. However, you might not want to offer each user total freedom to make any changes they like. User roles play a role in this.

Subscribers, contributors, authors, editors, admins, and super admins are just a few of the six user roles that are available. In this case, subscribers have the fewest rights while super admins have complete control over everything on the website.

For the sake of network security, it’s desirable to adopt these specific user roles rather than giving every user administrator rights.

If a hacker, for instance, gained access to an author’s account without authorization, they would only be able to edit, publish, and remove their own writings. They are unable to alter anything else.

However, a hacker can use a privilege escalation vulnerability to bypass these restrictions if it exists. They then begin evading user account control. They can now access features that are supposed to be available only to local administrators after just having author rights previously.

If this occurs, you never know what information the hackers might take or what nefarious deeds they might commit in your name!

Types of Privilege Escalation

Vertical and horizontal privilege escalation are the two categories into which privilege escalation may be divided. In vertical privilege escalation, the attacker attempts to take control of a higher-level account. However, with Horizontal Privilege Escalation (HPE), the hacker first takes control of an account before attempting to acquire access to system-level privileges. Both kinds of activities are accomplished by exploiting current operating system flaws.

Why Is It Important to Prevent Privilege Escalation Attacks?

Privilege escalation serves as a tool for attackers. It enables them to enter a system, maintain and expand their access, and engage in increasingly harmful activities. As an illustration, privilege escalation can turn a minor malware infection into a serious data breach.

Attackers can introduce new attack methods on a target system by escalating their privileges. For illustration, it might entail

Gaining access to connected systems elsewhere

Modifying the privileges or security settings

Gaining access to software or data on a system with more rights than the compromised account originally allowed

Obtaining root access to a target system or an entire network is possible in some circumstances

Investigating thoroughly is crucial when security teams suspect privilege escalation. Malware on sensitive systems, shady logins, and strange network communications are all indications of privilege escalation.

Depending on the organization’s compliance requirements, each privilege escalation incident must be handled as a severe security incident and may need to be reported to the authorities.

How Does Privilege Escalation Work?

One form of hack that takes place in a number of other hacking operations is privilege escalation. We’ll use an example to demonstrate how a privilege escalation attack operates.

Step 1 − Hack into any WordPress website user account

Consider that you manage a website with 10 members. Others are privileged users with admin capabilities who have enhanced access and can manage the entire website, while some are contributors and authors who can publish content.

Let’s say that a contributor account is utilizing the flimsy password “password123.” Hackers can easily deduce this. The hacker tries a variety of passwords to log into the account using a different kind of hacking technique known as a brute force assault (They can quickly test hundreds of passwords.) The hacker is successful since the password is so simple, and he or she now has contributor access.

A contributor, however, has limited rights. They are only able to create and edit their own posts; they are unable to publish them. This limits what the hacker may do with the account. Since having admin access would grant them total power over the website, they would want to elevate their credentials.

Step 2 − Upgrade Privileges by Disregarding Restrictions

There are plugins and themes installed on every WordPress website. Themes and plugins make the website stand out by enhancing the look and feel as well as the functionality.

A hacker may be able to increase the account’s privileges thanks to one of these vulnerabilities.

Step 3 − Execute the Attack

Privilege escalation is used to get ready for larger or more targeted attacks. The hacker might start pursuing their true objective once they have access to an admin account or the data they need. Some of the typical criminal behaviors these miscreants engage in include −

Stealing confidential and sensitive data from your company.

Stealing data and information from customers or clients, which they can then sell for money or use to carry out other hacks.

Stealing more login information from legitimate accounts on your website.

Deleting content and data from your website.

Sending unsolicited emails and messages to your consumers.

Using malware to sell illegal goods or drugs on your website.

Using malware to trick people into downloading it from your website.

Sending users to other websites, such as pornographic or spam sites, via your website.

Launching larger (DDoS) attacks from your website to knock down well-known websites.

Ethics And Artificial Intelligence: Driving Greater Equality

As artificial intelligence plays an ever greater role in our world, the question of ethics in our use of AI gains greater urgency. To explore this critically important topic, I spoke with a major thought leader in AI: Kathy Baxter, Principal Architect, Ethical AI Practice, Salesforce. In a wide ranging conversation, Baxter provided insight on the following:

1) To what extent is bias a major problem in today’s AI systems? What effect is that bias having?

2) On your blog, you write that “To determine if you are making decisions based on unfair criteria like race, gender, geography, or income, you need fairness through awareness. That means collecting sensitive variables to see correlations in the data but not make decisions based on those sensitive variables or their proxies.” But isn’t it true that once the data is collected, it will be used to create decisions?

3) You mention that companies need to commit to “creating and implementing AI responsibly and ethically.” How do you see this process evolving? On a related note, what are your thoughts around surveillance of employees and/or the ethical considerations of individual privacy?

4) What are steps that companies can take to improve the ethical foundation in their AI systems?

5) What about AI as it pertains to back-to-work solutions?

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Watch the full video interview:

Selected quotes from the full interview:

We are seeing bias come in in any place that humans are involved, because AI just reflects our own human tendencies.

The areas where we need to be most concerned about is anything that impacts human rights. So, our right to freedom, parole, bail, predictive policing. Access to benefits, like food and medical assistance, housing assistance. Privacy, so facial recognition. And of course medical, anything involving safety and health.

We know that those have a tendency to have bias if the data are not representative, if we are not collecting data from everyone, or if the data that we do collect represents our own historical biases, or if we’re not applying decision-making equally to everyone. So, certain communities being policed and more data recorded about them than other areas, and then [the AI bias] becomes unbalanced.

I think [bias in AI] is probably wider spread than what we may be aware of. Because we don’t have regulations or requirements on transparency and explainability, it makes it very hard to look across the spectrum at all of the different AI systems.

We don’t always even know when AI is being used, so a company might be using AI to screen resumes and highlight the resumes that they think are going to be the best candidates, and those are the ones that are forwarded to the hiring manager. And so the candidate may have zero insight that an AI had anything to do with their resume. So it’s that opaqueness that makes it difficult to know for sure just how prevalent these issues might be.

There are definitely companies out there that are leaders and have shared some very good practices for how they’re doing this. But on a regular basis, I have customers that will reach out to me and say, “Hey, we wanna do this too. We don’t even know where to begin. How do we find somebody that we could hire? What are the steps that we need to take?” And so this is very much an evolving area.

Paula Goldman, who is our Chief Ethical and Humane Use Officer, has likened this time in AI ethics to the 1980s with cybersecurity. So cybersecurity wasn’t even a thing. You didn’t do pen testing or red-teaming, and it wasn’t until those malware attacks came out that this became a thing. And so the security industry had to figure out what are the methodologies, what are the standards, how do we build this practice up?

And so the AI ethics industry is very much in a time similar to that today. We’re trying to figure out what are the best practices, can we agree upon a standard methodology for how we’ll identify bias in different types of models, what’s a safe threshold? ‘Cause we can never say a data set or a model is 100% bias-free. And so what’s that safe level that we agree upon?

In regulated industries, I think there’s more oversight. It may not be specific to AI, but whether it is a human deciding who gets a loan or an AI deciding, the regulation is the same. You can’t have biased decisions based on someone’s age, race, or gender. But in non-regulated industries, yes, right now, we don’t have a lot of guidelines and regulations, but that is changing.

There are a lot of different steps to take, but I think first and foremost, at the executive level, there really needs to be executive buy-in that this is going to be a priority, and create the incentive structure to support that.

And then you need the company to follow through. This really is an every employee hands-on-deck kind of effort.

It’s not just the ethicist on your team. Every single employee is responsible for thinking about, “Should we build this in the first place,” or the sales team, “Should I sell this feature for this particular use case?”

And so thinking through all of the different ethical pieces and understanding your individual responsibility is important. And so is having the training and the education available so that everybody feels empowered, they don’t feel like, “Well, I’m being held accountable for a metric that I don’t exactly understand how I’m supposed to meet.” So you need to come from both sides of the company to be successful.

We need regulation. We really do. Obviously for higher stakes AI, again, anything that impacts human rights like safety, privacy, freedom, we really need to prioritize those, and so we do see the EU in particular is very seriously putting together AI regulation.

We see in the US, there are a number of individual states that are putting forth their own regulations or down to the city level. So California, multiple cities have banned the use of facial recognition technology for various use cases. New York City right now, there’s a bill being passed to regulate the use of AI in hiring.

But not all of the people writing these policies and bills may understand all the complexities of AI. There’s the creators of it, and then there’s the implementers of it, and then there’s the individual users of it, and so regulations need to be specific to each one of those parties. So organizations like World Economic Forum, IEEE, Partnership on AI, each one of these organizations have been active in suggesting different types of regulations.

Artificial Intelligence In 2023: Urgency And Pragmatism

Where is artificial intelligence going in 2023? According to a recent Forrester research report, many companies feel an urgency to reap the benefits of AI. Indeed, artificial intelligence is seen as a propulsive driver of competitive success. If you’re not on the AI train, your competitors are leaving you at the station.

And yet there’s also a growing need to grapple with adopting AI in a pragmatic manner. AI can be wildly expensive, and companies have gotten burned doing “moonshot” projects. The mood is, “okay, we’ve heard that AI is big magic — now prove it to me.”

To shed light on the rapidly AI sector, I spoke with JP Gownder, Vice President, principal analyst, Forrester research. Gownder co-authored a recent report, AI 2023.

This discussion covers:

What is the current state of AI adoption?

Specific predictions for AI’s future.

How companies are purchasing artificial intelligence solutions – from AI companies?

Expectations for AI in 2023

Scroll down to see an edited transcript of highlights from”Data Analytics 2023.”

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Tech trends come and go, and they have their 15 minutes of fame. Yet it feels like artificial intelligence is more foundational – it’s the Mega trend that will eat all other trends. Agree or is that just hyperbole?

“I think that the future of AI is always bright, and that’s one of the problems we’ve had with AI. In the dawn of the computing, computer science era in the 1940s and ’50s, before we even had proper hardware, some computer scientists thought that we would solve the general AI problem by the 1970s. And of course, that didn’t happen.

“We look forward and then there was what was called an AI winter, which was a period of disillusionment when people realized that practically speaking, certain problems could not be overcome.

“But in the last three or four years, we’ve entered this new phase of AI development where not only have hardware and software become more capable to move to the cloud that you mentioned and other factors making us feel like AI could be done effectively. However, when we come right down to it, we need AI to be in service of something, hopefully it’s in service of improved efficiency or customer obsession or operational effectiveness. And we have fairly mixed record at the moment on that.

“Finally, I want to say, AI is such a plethora of different technologies that it’s all over the place. In some areas, AI will very quickly become table stakes. If you look at what Alibaba has done in the retail sector, or Amazon in terms of personalization and choice and predictive analytics around what people want to buy. Well look, that is becoming really powerful and important, but other areas of AI are quite lagging and are definitely hyperbole today.”

“So our data also shows that about 53% of data and analytics decision makers say that their company is in the implementing phase or they’ve already implemented some AI. But that is to say that within those organizations, that could be a small project.

“So 53% of companies are doing something, but that might be a Tensor Flow model running on one workstation for a data scientist. So it is again, rather variable.

“And I would say that in the grand scheme of things, we remain at an early stage of this, and there’s reasons for that, this is not easy to do well. We don’t necessarily have data hygiene that’s allowed us to tap into the right kind of data, we have these data silos and stuff. So, the foundation of AI being data, that’s a problem.

“We have a lack of governance, most companies don’t know from ethics to explainability to exactly who to participate in the process of overseeing governance. Very few companies have gone deeply down that road.”

How are companies actually purchasing AI solutions?

 “So there’s a wide range. Again, with AI being such a broad area, a couple of important factors here. Number one, it can be very challenging even for a large company to hire AI talent. So I was talking to an insurance company that’s global in span. It’s a huge company, based in the Midwest, however. This is not an area where there’s a lot of local talent. They could choose to hire someone who sits in San Francisco, but it might be a bit of an inhibitor. And it’s also the cost of that talent can grow to the millions of dollars.

“Secondarily, you may not even have the basic organizational capability to set something like this up. And so you may choose to go outside. But there could be other cases where you do have a data science team and what you’re doing is more incremental. You’re building using open-source kind of solutions like Tensor Flow, which is common in the ML and deep learning spaces. And maybe you can start internally.

“So what we’re finding is a distribution, but many companies do turn to external experts, companies that are able to offer data hygiene, data engineering services that are offering a variety of different kinds of analytical techniques. Or they’re working off of a big platform like Microsoft Azure or AWS, which have their own AI tools from which you can build applications.

“Finally, there’s also what you could think of as everyday or embedded AI, which is to say, ‘I am already using software and the vendor of that software has decided to add AI features to make my experience better.’

“When you log into the latest version of PowerPoint, you start building a slide, it actually tells you, ‘Here are some things you can do. Do you wanna do these things?’ And that’s actually powered by AI. So there’s a large span of different ways to do this from systems integrators, who are gonna do big projects, to existing software providers to building off of a platform to maybe a little bit of internal work.”

In closing, I’d like to get your thoughts about where we are in the bigger picture – AI going into 2023 – and if there’s any way for companies to get ahead of that wave as you see it happening.

We’re going to continue to see a move toward, ‘Prove it to me. What are the business results measurement?’ I think that some of the moonshot projects in a year in which we don’t necessarily expect recession, but reasonably slow economic growth, companies are turning pragmatic. That was the theme for 2023. We think that will continue into 2023.

“We also see that AI will be applied to, I think, more specific business problems rather than these broader ones. We will also see an element of AI riding in on certain other technologies. Principally or in the leading case, it’ll be robotic process automation.

“So those are some big trends that we see next year. AI will be a critical part of the conversation for enterprise tech, but it will become a pragmatic part of that conversation.”

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