Artificial Intelligence Agents

In the previous lecture, we discussed what we will be talking about in Artificial Intelligence and why those things are important. This lecture is all about how we will be talking about AI, i.E., the language, assumptions and concepts which will be common to all the topics we cover.

These notions should be considered before undertaking any large AI project. Hence, this lecture also serves to add to the systems engineering information you have/will be studying. For AI software/hardware, of course, we have to worry about which programming language to use, how to split the project into modules, etc. However, we also have to worry about higher level notions, such as: what does it mean for our program/machine to act rationally in a particular domain, how will it use knowledge about the environment, and what form will that knowledge take? All these things should be taken into consideration before we worry about actually doing any programming.

2.1 Autonomous Rational Agents

In many cases, it is inaccurate to talk about a single program or a single robot, as the combination of hardware and software in some intelligent systems is considerably more complicated. Instead, we will follow the lead of Russell and Norvig and describe AI through the autonomous, rational intelligent agents paradigm. We're going to use the definitions from chapter 2 of Russell and Norvig's textbook, starting with these two:

An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.

A rational agent is one that does the right thing.

We see that the word 'agent' covers humans (where the sensors are the senses and the effectors are the physical body parts) as well as robots (where the sensors are things like cameras and touch pads and the effectors are various motors) and computers (where the sensors are the keyboard and mouse and the effectors are the monitor and speakers).

To determine whether an agent has acted rationally, we need an objective measure of how successful it has been and we need to worry about when to make an evaluation using this measure. When designing an agent, it is important to think hard about how to evaluate it's performance, and this evaluation should be independent from any internal measures that the agent undertakes (for example as part of a heuristic search - see the next lecture). The performance should be measured in terms of how rationally the program acted, which depends not only on how well it did at a particular task, but also on what the agent experienced from its environment, what the agent knew about its environment and what actions the agent could actually undertake.

Acting Rationally

Al Capone was finally convicted for tax evasion. Were the police acting rationally?

To answer this, we must first look at how the performance of police forces is viewed: arresting and convicting the people who have committed a crime is a start, but their success in getting criminals off the street is also a reasonable, if contentious, measure. Given that they didn't convict Capone for the murders he committed, they failed on that measure. However, they did get him off the street, so they succeeded there. We must also look at the what the police knew and what they had experienced about the environment: they had experienced murders which they knew were undertaken by Capone, but they had not experienced any evidence which could convict Capone of the murders. However, they had evidence of tax evasion. Given the knowledge about the environment that they can only arrest if they have evidence, their actions were therefore limited to arresting Capone on tax evasion. As this got him off the street, we could say they were acting rationally.

This answer is controversial, and highlights the reason why we have to think hard about how to assess the rationality of an agent before we consider building it.

To summarise, an agent takes input from its environment and affects that environment. The rational performance of an agent must be assessed in terms of the task it was meant to undertake, it's knowledge and experience of the environment and the actions it was actually able to undertake. This performance should be objectively measured independently of any internal measures used by the agent.

In English language usage, autonomy means an ability to govern one's actions independently. In our situation, we need to specify the extent to which an agent's behaviour is affected by its environment. We say that:

The autonomy of an agent is measured by the extent to which its behaviour is determined by its own experience.

At one extreme, an agent might never pay any attention to the input from its environment, in which case, its actions are determined entirely by its built-in knowledge. At the other extreme, if an agent does not initially act using its built-in knowledge, it will have to act randomly, which is not desirable. Hence, it is desirable to have a balance between complete autonomy and no autonomy. Thinking of human agents, we are born with certain reflexes which govern our actions to begin with. However, through our ability to learn from our environment, we begin to act more autonomously as a result of our experiences in the world. Imagine a baby learning to crawl around. It must use in-built information to enable it to correctly employ its arms and legs, otherwise it would just thrash around. However, as it moves, and bumps into things, it learns to avoid objects in the environment. When we leave home, we are (supposed to be) fully autonomous agents ourselves. We should expect similar of the agents we build for AI tasks: their autonomy increases in line with their experience of the environment.

2.2 RHINO, the Museum Tour-Guide Agent

We will mostly be dealing with agents based inside computers, rather than robots based in the real world. However, the museum tour guide robot mentioned in the first lecture offers an ideal example of an autonomous agent which we will use to illustrate various concepts in the rest of this lecture. This robot was called RHINO.

RHINO's job was to inform visitors to the Museum about various exhibits. To do this, it had to perform two main tasks: (i) move safely from exhibit to exhibit and (ii) display information and answer questions about each exhibit it visited. The project was very successful - in an operational time of 47 hours, covering 18.6 kilometres, the software made only one mistake: a minor collision which caused no harm.

2.3 Internal Structure of Agents (Slide 16)

We have looked at agents in terms of their external influences and behaviours: they take input from the environment and perform rational actions to alter that environment. We will now look at some generic internal mechanisms which are common to intelligent agents.

The program of an agent is the mechanism by which it turns input from the environment into an action on the environment. The architecture of an agent is the computing device (including software and hardware) upon which the program operates. On this course, we mostly concern ourselves with the intelligence behind the programs, and do not worry about the hardware architectures they run on. In fact, we will mostly assume that the architecture of our agents is a computer getting input through the keyboard and acting via the monitor.

RHINO consisted of the robot itself, including the necessary hardware for locomotion (motors, etc.) and state of the art sensors, including laser, sonar, infrared and tactile sensors. RHINO also carried around three on-board PC workstations and was connected by a wireless Ethernet connection to a further three off-board SUN workstations. In total, it ran up to 25 different processes at any one time, in parallel. The program employed by RHINO was even more complicated than the architecture upon which it ran. RHINO ran software which drew upon techniques ranging from low level probabilistic reasoning and visual information processing to high level problem solving and planning using logical representations.

An agent's program will make use of knowledge about its environment and methods for deciding which action to take (if any) in response to a new input from the environment. These methods include reflexes, goal based methods and utility based methods.

Knowledge of the Environment (SLIDE 18)

-Knowledge of Environment (World)

– -Different to sensory information from environment

lWorld knowledge can be (pre)-programmed in

– -Can also be updated/inferred by sensory information

-Using knowledge to inform choice of actions:

– - Use knowledge of current state of the world

– -Use knowledge of previous states of the world

– -Use knowledge of how its actions change the world

-Example: Chess agent

– -World knowledge is the board state (all the pieces)

– -Sensory information is the opponents move

– -It's moves also change the board state ( previous states, …)

We must distinguish between knowledge an agent receives through it's sensors and knowledge about the world from which the input comes. Knowledge about the world can be programmed in, and/or it can be learned through the sensor input. For example, a chess playing agent would be programmed with the positions of the pieces at the start of a game, but would maintain a representation of the entire board by updating it with every move it is told about through the input it receives. Note that the sensor inputs are the opponent's moves and this is different to the knowledge of the world that the agent maintains, which is the board state.

There are three main ways in which an agent can use knowledge of its world to inform its actions. If an agent maintains a representation of the world, then it can use this information to decide how to act at any given time. Furthermore, if it stores its representations of the world, then it can also use information about previous world states in its program. Finally, it can use knowledge about how its actions affect the world.

The RHINO agent was provided with an accurate metric map of the museum and exhibits beforehand, carefully mapped out by the programmers. Having said this, the layout of the museum changed frequently as routes became blocked and chairs were moved. By updating it's knowledge of the environment, however, RHINO consistently knew where it was, to an accuracy better than 15cm. RHINO didn't move objects other than itself around the museum. However, as it moved around, people followed it, so its actions really were altering the environment. It was because of this (and other reasons) that the designers of RHINO made sure it updated its plan as it moved around.

Reflexes (Slide 20)- Action on the world (Slide 20)

– -In response only to a sensor input

– -Not in response to world knowledge

- Humans – flinching, blinking

If an agent decides upon and executes an action in response to a sensor input without consultation of its world, then this can be considered a reflex response. Humans flinch if they touch something very hot, regardless of the particular social situation they are in, and this is clearly a reflex action. Similarly, chess agents are programmed with lookup tables for openings and endings, so that they do not have to do any processing to choose the correct move, they simply look it up. In timed chess matches, this kind of reflex action might save vital seconds to be used in more difficult situations later.

Unfortunately, relying on lookup tables is not a sensible way to program intelligent agents: a chess agent would need 35100 entries in its lookup table (considerably more entries than there are atoms in the universe). And if we remember that the world of a chess agent consists of only 32 pieces on 64 squares, it's obvious that we need more intelligent means of choosing a rational action.

For RHINO, it is difficult to identify any reflex actions. This is probably because performing an action without consulting the world representation is potentially dangerous for RHINO, because people get everywhere, and museum exhibits are expensive to replace if broken!

One possible way to improve an agent's performance is to enable it to have some details of what it is trying to achieve. If it is given some representation of the goal (e.G., some information about the solution to a problem it is trying to solve), then it can refer to that information to see if a particular action will lead to that goal. Such agents are called goal-based. Two tried and trusted methods for goal-based agents are planning (where the agent puts together and executes a plan for achieving its goal) and search (where the agent looks ahead in a search space until it finds the goal). Planning and search methods are covered later in the course.

In RHINO, there were two goals: get the robot to an exhibit chosen by the visitors and, when it gets there, provide information about the exhibit. Obviously, RHINO used information about its goal of getting to an exhibit to plan its route to that exhibit.

Utility Functions (Slide 22)- Knowledge of a goal may be difficult to pin down

– -For example, checkmate in chess (king can't move)

- But some agents have localised measures

– -Utility functions measure value of world states

– -Choose action which best improves utility (rational!)

– - In search, this is "Best First"

A goal based agent for playing chess is infeasible: every time it decides which move to play next, it sees whether that move will eventually lead to a checkmate. Instead, it would be better for the agent to assess it's progress not against the overall goal, but against a localised measure. Agent's programs often have a utility function which calculates a numerical value for each world state the agent would find itself in if it undertook a particular action. Then it can check which action would lead to the highest value being returned from the set of actions it has available. Usually the best action with respect to a utility function is taken, as this is the rational thing to do. When the task of the agent is to find something by searching, if it uses a utility function in this manner, this is known as a best-first search.

RHINO searched for paths from its current location to an exhibit, using the distance from the exhibit as a utility function. However, this was complicated by visitors getting in the way.

2.4 Environments

We have seen that intelligent agents should take into account certain information when choosing a rational action, including information from its sensors, information from the world, information from previous states of the world, information from its goal and information from its utility function(s). We also need to take into account some specifics about the environment it works in. On the surface, this consideration would appear to apply more to robotic agents moving around the real world. However, the considerations also apply to software agents which are receiving data and making decisions which affect the data they receive - in this case we can think of the environment as the flow of information in the data stream. For example, an AI agent may be employed to dynamically update web pages based on the requests from internet users.

We follow Russell and Norvig's lead in characterising information about the environment:

Accessibility (Slide 24)Is everything an agent requires to choose its actions available to it via its sensors?

– -If so, the environment is fully accessible

- If not, parts of the environment are inaccessible

– -Agent must make informed guesses about world

In some cases, certain aspects of an environment which should be taken into account in decisions about actions may be unavailable to the agent. This could happen, for instance, because the agent cannot sense certain things. In these cases, we say the environment is partially inaccessible. In this case, the agent may have to make (informed) guesses about the inaccessible data in order to act rationally.

The builders of RHINO talk about "invisible" objects that RHINO had to deal with. These included glass cases and bars at various heights which could not be detected by the robotic sensors. These are clearly inaccessible aspects of the environment, and RHINO's designers took this into account when designing its programs.

If we can determine what the exact state of the world will be after an agent's action, we say the environment is deterministic. In such cases, the state of the world after an action is dependent only on the state of the world before the action and the choice of action. If the environment is non-deterministic, then utility functions will have to make (informed) guesses about the expected state of the world after possible actions if the agent is to correctly choose the best one.

RHINO's world was non-deterministic because people moved around, and they move objects such as chairs around. In fact, visitors often tried to trick the robot by setting up roadblocks with chairs. This was another reason why RHINO's plan was constantly updated.

If an agent's current choice of action does not depend on its past actions, then the environment is said to be episodic. In non-episodic environments, the agent will have to plan ahead, because it's current action will affect subsequent ones.

Considering only the goal of getting to and from exhibits, the individual trips between exhibits can be seen as episodes in RHINO's actions. Once it had arrived at one exhibit, how it got there would not normally affect its choices in getting to the next exhibit. If we also consider the goal of giving a guided tour, however, RHINO must at least remember the exhibits it had already visited, in order not to repeat itself. So, at the top level, its actions were not episodic.

An environment is static if it doesn't change while an agent's program is making the decision about how to act. When designing agents to operate in dynamic (non-static) environments, the underlying program may have to refer to the changing environment while it deliberates, or to anticipate the change in the environment between the time when it receives an input and when it has to take an action.

RHINO was very fast in making decisions. However, because of the amount of visitor movement, by the time RHINO had planned a route, that plan was sometimes wrong because someone was now blocking the route. However, because of the speed of decision making, instead of referring to the environment during the planning process, as we have said before, the designers of RHINO chose to enable it to continually update its plan as it moved.

- Nature of sensor readings / choices of action (Slide 28)

–- Sweep through a range of values (continuous)

–- Limited to a distinct, clearly defined set (discrete)

The nature of the data coming in from the environment will affect how the agent should be designed. In particular, the data may be discrete (composed of a limited number of clearly defined parts) or continuous (seemingly without discernible sections). Of course, given the nature of computer memory (in bits and bytes), even streaming video can be shoe-horned into the discrete category, but an intelligent agent will probably have to deal with this as if it is continuous. The mathematics in your agent's programs will differ depending on whether the data is taken to be discrete or continuous.

RHINO's data came from 3d space, hence was considered continuous.

Multi-Agent Systems

The word 'agent' is extremely popular in AI at the moment, and you will come across research on multi-agent systems. This approach to AI is to break a task into subtasks which have to be undertaken simultaneously, and give each subtask to a different autonomous agent. The agents can communicate in order to co-operate and compete on their tasks. This approach has been shown to be very effective on certain problems and is currently very influential in AI.

I recommend Mike Wooldridge's book: An Introduction to MultiAgent Systems as an excellent introductory text.

© Simon Colton 2004.

Algorithmic, Game-Theoretic, And Logical Foundations

This exciting and pioneering new overview of multiagent systems, which are online systems composed of multiple interacting intelligent agents, i.E., online trading, offers a newly seen computer science perspective on multiagent systems, while integrating ideas from operations research, game theory, economics, logic, and even philosophy and linguistics. The authors emphasize foundations to create a broad and rigorous treatment of their subject, with thorough presentations of distributed problem solving, game theory, multiagent communication and learning, social choice, mechanism design, auctions, cooperative game theory, and modal logics of knowledge and belief. For each topic, basic concepts are introduced, examples are given, proofs of key results are offered, and algorithmic considerations are examined. An appendix covers background material in probability theory, classical logic, Markov decision processes and mathematical programming. Written by two of the leading researchers of this engaging field, this book will surely serve as THE reference for researchers in the fastest-growing area of computer science, and be used as a text for advanced undergraduate or graduate courses.

"This is by far the best text in the field of multiagent systems, one of the fastest-growing areas in computer science."Stuart Russell, University of California at Berkeley

"The integration of methodologies that study different aspects of interactive strategic systems is of vital importance in modern society. Through excellent side-by-side presentation of the main approaches in computer science, game theory and economics, this pioneering textbook is a major advance towards the education of a better-equipped generation of computer scientists as well as social scientists."Ehud Kalai, Northwestern University

"This is a rich and comprehensive text on multiagent systems, written by two of the leading researchers in the area in an engaging and accessible style. It is unique in covering the diverse foundations of multiagent systems, including logic. Its extensive treatment of the interplay between computer science and game theory will define how the subject should be taught. I recommend the book for graduate students and advanced undergraduates, as well as researchers in both computer science and economics trying to learn the basics of the field."Joseph Halpern, Cornell University

"With the emergence of the Internet, the focus of much of the research in computer science and in artificial intelligence is shifting from the the study of the single program to the study of the interactions among different computers and programs. Multiagent Systems presents for the first time this cutting-edge research in a textbook form. The book transcends the traditional boundaries of artificial intelligence and touches all aspects of multiagent systems: from artificial intelligence to algorithms to game theory, to logic, and beyond. Written by leaders in this research area, this book is certain to become the textbook of choice for classes on multiagent systems."Noam Nisan, Hebrew University

"Shoham and Leyton-Brown traverse several disciplines to bring together the most salient and useful technical principles for understanding multiagent systems. This text is the first to provide computer scientists with a comprehensive treatment of the mathematical machinery they need to analyze systems of autonomous agents, integrating their computational and strategic dimensions."Michael Wellman, University of Michigan

"... A useful reference volume for researchers and a good foundational body of knowledge for those just starting their exploration of the field. It is rich in examples... With its 13 chapters, this volume definitely deserves a handy place on the shelves of those studying MAS... Highly recommended.G. Trajkovski, Laureate Higher Education Group for Choice Magazine

"The book with its comprehensive and broad treatment promises to become the standard textbook on multiagent systems. It provides a useful and easy to read survey of a rapidly growing field. The text is one of the clearest expositions of concepts in game theory. The book easily deserves to be on the shelf of any modern theoretical computer scientist."Haris Aziz, SIGACT News

Top 10 Artificial Intelligence Executives In 2023

Artificial intelligence (AI) isn't the future; it's here. It now serves as a crucial part of the operations of many enterprises, helping them keep up with the ever-changing digital infrastructure. Similarly, AI-focused federal solutions help the government in improving the national services related to security, defense, healthcare, education, and other sectors.

What is artificial intelligence?

Artificial intelligence is the capability of advanced computer systems to perform tasks usually done by humans. AI uses a combination of machine learning, language processing, and speech and vision recognition to simulate human intelligence, emotions, the ability to reason, and past experiences. Using AI improves productivity and increases efficiency in company operations.

To address the ever-increasing need for advanced AI technology services in commercial and federal markets, here are the top artificial intelligence executives in 2023.

1. Vincent Bridgeman, Senior Vice President of National Security Services at Redhorse Corporation

2. Ro Dhanda, Vice President of Public Sector at H2O.Ai

3. William Sullivan, Vice President and General Manager of U.S. Federal at Denodo

4. Dan Wilbricht, General Manager of Public Sector at Devo Technology

5. Walter Maikish, Vice President of Pure Federal at Pure Storage

6. Anthony Robbins, Vice President of Federal at NVIDIA

7. Cedric George, Director of DoD Strategic and Mission Acceleration Team at Amazon Web Services (AWS)

8. Dr. Tamara L. Nall, Founder & Chief Executive Officer at The Leading Niche

9. Kristen Summers, Operating Unit Chief Technology Officer at Microsoft Federal

10. Margaret Graves, Senior Fellow at IBM Center for the Business of Government

1. Vincent Bridgeman, Senior Vice President of National Security Services at Redhorse Corporation

Photo from Redhorse Corporation Official Website

Vincent Bridgeman is the Senior Vice President of National Security at Redhorse Corporation. He was promoted to the position on February 18, 2023, after serving as the Vice President for nine years and Vice President of Intelligence Services for six years.

For over two decades, Bridgeman served as an intelligence officer in the U.S. Marine Corps and represented the Department of Defense in the In-Q-Tel. For a brief period, he also served as an associate at Booz Allen Hamilton before joining Redhorse Corporation.

Bridgeman has built a portfolio in intelligence, tactical, operational, and strategic functions throughout his tenure. His military and commercial intelligence expertise has earned him recognition as an innovative thinker for U.S. Intelligence and technology programs.

Redhorse Corporation's Senior Vice President of National Security Services Roles and Responsibilities

Redhorse Corporation's Senior Vice President of National Security Services handles the company's information technology services and contracting activities for the government's national security priorities. Bridgeman is integral in shaping the company's artificial intelligence, machine learning, and digital transformation capabilities.

About Redhorse Corporation

Redhorse Corporation is a management consulting services company for technology and digital solutions. Established in 2008, the company has received multiple awards for its broad scope of services related to the federal government's civilian, defense, intelligence, homeland security, and other functions.

2. Ro Dhanda, Vice President of Public Sector at H2O.Ai

Photo of Ro Dhanda at LinkedIn

As the Vice President of Public Sector at H2O.Ai, Ro Dhanda handles the company's artificial intelligence, machine learning capabilities, and other technology platforms for the federal government. He has served in the position since February 2021.

Previously, Dhanda held increasing responsibilities for technology organizations such as Information Access, NCompass Labs, and Microsoft. His career as an executive began at Iron Mountain, where he served as the Vice President of Business Development. He then became one of the artificial intelligence executives in Daon, Dell, and Snowflake Computing.

Building his experiences in the technology sector, Dhanda is considered an industry leader in the IT and services industries. He is one of the pioneers of open-source data science and machine learning, making him a distinguished professional in Silicon Valley.

H2O.Ai's Vice President of Public Sector Roles and Responsibilities

H2O.Ai's Vice President of Public Sector is responsible for democratizing AI solutions for citizens and residents of the United States. Dhanda leads the software development of hybrid cloud platforms, strategic partnership management, and other technical functions to solve complex problems and operations in public functions of society. 

About H2O.Ai

H2O.Ai is an artificial intelligence software solutions provider based in Mountain View, California. The company is on a mission to democratize AI for everyone, enabling them to accelerate the discovery of new ideas. Partnering with over 20,000 global organizations, H2O.Ai is a trusted company for the biggest corporations worldwide.

3. William Sullivan, Vice President and General Manager of U.S. Federal at Denodo

Photo of William Sullivan at LinkedIn

William Sullivan is the Vice President and General Manager of U.S. Federal at Denodo. As the leader in data virtualization and data fabric technology, he is responsible for driving 83% growth year-over-year by the end of the third quarter of the federal market.

Sullivan has been among the top artificial intelligence executives, building his leadership portfolio since 2002. After his sabbatical from 2019 to 2020,  he rejoined the federal government contracting industry as the Principal at Quinn Consulting.

As a key AI executive in federal contracting activities, Sullivan advocates for the widespread adoption of AI applications and innovation of systems to overcome architectural and security limitations for hybrid clouds and multi-clouds.

Denodo's Vice President and General Manager of U.S. Federal Roles and Responsibilities

Denodo's Vice President and General Manager of U.S. Federal manages the multi-cloud development of systems and applications for the federal government. Sullivan is also responsible for the company's marketing, sales, and contractual relationships with the federal, local, and state governments.

About Denodo

Denodo is an award-winning data management enterprise. The company's advanced systems for data integration, management, hybrid/multi-cloud integration, and other artificial intelligence platforms offer 400% ROI and millions of dollars in benefits for its customers across large enterprises and mid-market companies.

4. Dan Wilbricht, General Manager of Public Sector at Devo Technology

Photo of Dan Wilbricht at LinkedIn

Dan Wilbricht has served as the General Manager of Public Sector at Devo Technology since August 2021. He builds processes, systems, and data management and heads the company's development for cybersecurity and AI solutions for the federal government.

During his early career, Wilbricht handled sales and business development for various companies before engaging in the federal systems of tech giants such as Dell Software Group, Red Hat, and HP. In addition, he has served in several executive leadership roles and has been an advisor for technology companies and organizations.

Wilbricht's addition to Devo Technology's executive team has seen a significant performance leap from a decent-sized enterprise to a unicorn company. Within months of his appointment, he helped the company receive multiple investments and funding. 

Devo Technology's General Manager of Public Sector Roles and Responsibilities

Since joining as the General Manager of Public Sector at Devo Technology, Dan Wilbricht has set up a channel and federal system instigator-focused teams to handle GSA schedules and other government contracts. His core contributions include increasing the company's valuation from $800 million to $2.5 billion.

About Devo Technology

Devo Technology is a cloud-native security analytics platform based in Cambridge, Massachusetts. The company is an expert in secure data management, ensuring its clients and partners of cybersecurity and digital protection. Devo Technology is backed by Insight Partners, Georgian, and Bessemer Venture Partners. 

5. Walter Maikish, Vice President of Pure Federal at Pure Storage

Photo of Walter Maikish at LinkedIn

Walter Maikish serves as the Vice President of Pure Federal at Pure Storage. He works closely with federal departments and agencies to deliver data-driven management solutions that can help the government build a better world with data.

Previously, Maikish served as the Business Analyst of Financial Services Sector East at IBM and increasing responsibilities at Cisco, ultimately serving as the Operations Director of Federal Civilian. In his tenure, he has built experiences in a broad scope of federal affairs, including entitlement, scientific, legal, and veteran affairs.

Since joining Pure Storage in August 2021, Maikish has led the company's business engagement efforts with the federal government. He has helped multiple government agencies to realize their digital transformation goals for society.

Pure Storage's Vice President of Pure Federal Roles and Responsibilities

As the Vice President of Pure Federal at Pure Storage, Walter Maikish manages the company's business engagements with the federal government. Under his leadership, his team delivers data-enabled solutions for mission-critical, modern analytics, and multi-cloud environments.

About Pure Storage

Pure Storage is a developer of all-flash data storage hardware and software products. Based in Mountain View, California, the company offers secure data solutions to enable SaaS companies, cloud service providers, and the public sector, among other industries.

6. Anthony Robbins, Vice President of Federal at NVIDIA

Photo from NVIDIA Official Website

Anthony Robbins is the Vice President of Federal at NVIDIA. He has served in the top artificial intelligence executive position since October 2017, running, building, and leading the company's digital, cloud, and AI solutions for federal markets.

Robbins has been a renowned executive in the technology sector for nearly three decades. He has served in tech giants, including Silicon Graphics, Sun Microsystems, Oracle Corporation, Brocade, AT&T, and AFCEA DC. His career focuses on meeting the needs and requirements of the government for class-leading digital solutions.

As a key executive at NVIDIA, Robbins played a crucial role in delivering more than $15 billion in revenue. His leadership has provided digital IT and artificial intelligence solutions for the government's warfighters and citizens.

NVIDIA's Vice President of Federal Roles and Responsibilities

NVIDIA's Vice President of Federal is responsible for helping the federal government with GPU and CUDA programming models. Robbins works closely with government agencies, cloud service providers, federal systems integrators, universities, and start-up communities to usher in a new era of artificial intelligence.

About NVIDIA

NVIDIA Corporation is an artificial intelligence software pioneer for transforming the largest industries in the world. Headquartered in Santa Clara, California, the company is best known for producing graphics processing units (GPUs), application programming interfaces (APIs, and system-on-a-chip units (SoCs) for mobile devices, computers, and vehicles.

7. Cedric George, Director of DoD Strategic and Mission Acceleration Team at Amazon Web Services (AWS)

Photo from U.S. Air Force Official Website

Cedric George is a veteran of the U.S. Air Force with nearly three decades of experience in various agency divisions. Currently, he is the Director of the Department of Defense's Strategic and Mission Acceleration Team at Amazon Web Services.

George was with the Armed Forces for three decades, assuming increasing responsibilities in different squadrons, operations, and directorates. He served as the  Director of Logistics for over a year before retirement in 2019. After serving in the military, he served as the Chief Executive Officer at George Consulting Group, LLC.

Since joining AWS in May 2020, George's position has been crucial to AWS' activities with the Department of Defense and other defense-related agencies of the U.S. Government. In 2021, he showcased his expertise during the U.S. Air Force's On-Ramp 4 technical demonstration, which experiments on cloud capabilities for faster and more effective mission capabilities in the field.

Amazon Web Services' Director of DoD Strategic and Mission Acceleration Team Roles and Responsibilities

Amazon Web Services' Director of DoD Strategic and Mission Acceleration Team handles the cloud computing capabilities of the company for the government's defense communities. George develops strategies using AI technologies to achieve better mission outcomes and enhance experiences by military personnel.

About Amazon Web Services

Amazon Web Services is a cloud computing platform offering over 200 fully featured services for millions of customers worldwide. The company boasts lower costs and more agile on-demand cloud computing software to fit the needs of fast-growing start-ups, large enterprises, and federal government agencies.

8. Dr. Tamara L. Nall, Founder & Chief Executive Officer at The Leading Niche

Photo of Dr. Tamara L. Nall at LinkedIn

As the Founder and Chief Executive Officer of The Leading Niche, Dr. Tamara L. Nall leads an award-winning company providing cutting-edge artificial intelligence, internally labeled as 'actionable intelligence,' for commercial, defense, and civilian sectors.

Dr. Nall is one of the pioneers of data analytics and artificial intelligence, engaging in the industry since 1992. Her previous experiences include serving as the International Business Analyst at Georgia-Pacific Resins, Inc., Independent Consultant at Hilton International, and Senior Associate at Booz & Company. 

With decades of experience in the industry, Dr. Nall has received numerous awards for her contributions to the digital and cybersecurity sectors. Her leadership and expertise have grown the start-up company into one of the top artificial intelligence consulting businesses for a broad scope of government markets.

The Leading Niche's Chief Executive Officer Roles and Responsibilities

The Leading Niche's Chief Executive Officer helps companies remain competitive and sustainable in the ever-changing marketplaces for different industries. Since founding the company in 2007, Dr. Nall has built a comprehensive portfolio for big data, cybersecurity, intelligence, C4ISR, regulatory compliance, examination consulting, and more. 

About The Leading Niche

The Leading Niche is a New York City-based business consulting and services firm recognized for using data and cutting-edge 'actionable intelligence.' The company supports the commercial and government sectors across domestic and international markets, spanning North America, Europe, and Africa.

9. Kristen Summers, Operating Unit Chief Technology Officer at Microsoft Federal

Photo of Kristen Summers at LinkedIn

Kristen Summers has served as the Operating Unit Chief Technology Officer at Microsoft Federal since January 2022. Working in the federal sector of one of the biggest tech companies in the world, she handles the enterprise's technology and artificial intelligence businesses for the United States government.

Before joining Microsoft Federal, Summers worked as the Research Engineer at Thomson Corporation and Technical Director at CACI. She also held increasing responsibilities at IBM for over seven years. She ultimately served as the Distinguished Engineer with responsibilities of Artificial Intelligence Chief Technology Officer for Data and AI Expert Labs and Learning.

Having over two decades of tenure, Summers has accomplished multiple credentials and certifications, such as Professional Certification: Level 3 – Distinguished Data Scientist from The Open Group, Data Science Profession Certification – Level 3 Thought Leader, and IBM Consulting Profession Certification – Thought Leader from IBM.

Microsoft Federal's Operating Unit Chief Technology Officer Roles and Responsibilities

As Microsoft Federal's Operating Unit Chief Technology Officer, Kristen Summers creates artificial intelligence solutions to enable the federal government to maximize its data. Leading the operating unit's technology services, she combines AI and analytics with state-of-the-art tools to understand and solve complex situations in the government.

About Microsoft Federal

Microsoft Federal handles the public sector solutions of the Redmond-based technology corporation. The company offers mission comprehension, engineering, and technology services to the U.S. Government and its operations abroad. Some agencies with which the company collaborates include the U.S. Postal Service, the U.S. Navy, and the Choctaw Nation. 

10. Margaret Graves, Senior Fellow at IBM Center for the Business of Government

Photo from IBM Center for the Business of Government Official Website

Joining IBM Center for the Business of Government as the Senior Fellow in July 2021, M