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Contributed Commentary by Mona G. Flores, MD

June 24, 2021 | If theres anything the global pandemic has taught healthcare providers, it is the importance of timely and accurate data analysis and being ready to act on it. Yet these same organizations must move within the bounds of patient rights regulations, both existing and emerging, making it harder to access the data needed for building relevant artificial intelligence (AI) models.

One way to get around this constraint is de-identify the data before curating it into one centralized location where it can be used for AI model training.

An alternative option would be to keep the data where it originated and learn from this data in a distributed fashion without the need for de-identification. New companies are being created to do this, such as US startup Rhino Health. It recently raised $5 million (US) to connect hospitals with large databases from diverse patient populations to train and validate AI models using Federated Learning while ensuring privacy.

Other companies are following suit. This is hardly surprising considering that the global market for big data analytics in health care was valued at $16.87 billion in 2017 and is projected to reach $67.82 billion by 2025, according to a report from Allied Market Research.

Federated Learning Entering the Mainstream

AI already has led to disruptive innovations in radiology, pathology, genomics, and other fields. To expand upon these innovations and meet the challenge of providing robust AI models while ensuring patient privacy, more healthcare organizations are turning to federated learning.

With Federated Learning, Institutions hide their data and seek the knowledge. Federated Learning brings the AI model to the local data, trains the model in a distributed fashion, and aggregates all the learnings along the way. In this way, no data is exchanged whatsoever. The only exchange occurring is model gradients.

Federated Learning comes in many flavors. In the client-server model employed by Clara FL today, the server aggregates the model gradients it receives from all of the participating local training sites (Client-sites) after each iteration of training. The aggregation methodology can vary from a simple weighted average to more complex methods chosen by the administrator of the FL training.

The end result is a more generalizable AI model trained on all the data from each one of the participating institutions while maintaining data privacy and sovereignty.

Early Federated Learning Work Shows Promise

New York-based Mount Sinai Health Systems recently used federated learning to analyze electronic health records to better predict how COVID-19 patients will progress using the AI model and data from five separate hospitals. The federated learning process allowed the model to learn from multiple sources without exposing patient data.

The Federated model outperformed local models built using data from each hospital separately and it showed better predictive capabilities.

In a larger collaboration among NVIDIA and 20 hospitals, including Mass General Brigham, National Institutes of Health in Bethesda, and others in Asia and Europe, the work focused on creating a triage model for COVID-19. The FL model predicted on initial presentation if a patient with symptoms suspicious for COVID-19 patient will end up needing supplemental oxygen within a certain time window.

Considerations and Coordination

While Federated learning addresses the issue of data privacy and data access, it is not without its challenges. Coordination between the client sites needs to happen to ensure that the data used for training is cohesive in terms of format, pre- processing steps, labels, and other factors that can affect training. Data that is not identically distributed at the various client sites can also pose problems for training, and it is an area of active research. And there is also the question of how the US Food and Drug Administration, European Union, and other regulatory bodies around the world will certify models trained using Federated Learning. Will they require some way of examining the data that went into training to be able to reproduce the results of Federated Learning, or will they certify a model based on its performance on external data sets?

In January, the U.S. Food and Drug Administration updated its action plan for AI and machine learning in software as a medical device, underscoring the importance of inclusivity across dimensions like sex, gender, age, race, and ethnicity when compiling datasets for training and testing. The European Union also includes a right to explanation from AI systems in GDPR.

It remains to be seen how they will rule on Federated Learning.

AI in the Medical Mainstream

As Federated Learning approaches enter the mainstream, hospital groups are banking on Artificial Intelligence to improve patient care, improve the patient experience, increase access to care, and lower healthcare costs. But AI needs data, and data is money. Those who own these AI models can license them around the world or can share in commercial rollouts. Healthcare organizations are sitting on a gold mine of data. Leveraging this data securely for AI applications is a golden goose, and those organizations that learn to do this will emerge the victors.

Dr. Mona Flores is NVIDIAs Global Head of Medical AI. She brings a unique perspective with hervaried experience in clinical medicine, medical applications, and business. She is a boardcertified cardiac surgeon and the previous Chief Medical Officer of a digital health company.She holds an MBA in Management Information Systems and has worked on Wall Street. Herultimate goal is the betterment of medicine through AI. She can be reached at mflores@nvidia.com.

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Data Privacy Is Key to Enabling the Medical Community to Leverage Artificial Intelligence to Its Full Potential - Bio-IT World

Despite AIs promise, many companies are not realizing the full potential of machine learning and other AI functions. Why? Ironically, it turns out that the issue is, in large part...people. Inefficient workflows can hold companies back from getting the full value of their AI implementations.

For example, data scientists can face challenges getting the resources and data they need to build machine learning models. They may have trouble collaborating with their teammates. And they have many different open source tools to manage, while application developers sometimes need to entirely recode models that data scientists develop before they can embed them into their applications.

With a growing list of open source AI tools, IT ends up spending more time supporting the data science teams by continuously updating their work environments. This issue is compounded by limited standardization across how data science teams like to work.

Finally, senior executives might not be able to visualize the full potential of their companys AI investments. Consequently, they dont lend enough sponsorship and resources to creating the collaborative and integrated ecosystem required for AI to be successful.

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What is Artificial Intelligence (AI)? | Oracle

The journal of Artificial Intelligence (AIJ) welcomes papers on broad aspects of AI that constitute advances in the overall field including, but not limited to, cognition and AI, automated reasoning and inference, case-based reasoning, commonsense reasoning, computer vision, constraint processing, ethical AI, heuristic search, human interfaces, intelligent robotics, knowledge representation, machine learning, multi-agent systems, natural language processing, planning and action, and reasoning under uncertainty. The journal reports results achieved in addition to proposals for new ways of looking at AI problems, both of which must include demonstrations of value and effectiveness.

Papers describing applications of AI are also welcome, but the focus should be on how new and novel AI methods advance performance in application areas, rather than a presentation of yet another application of conventional AI methods. Papers on applications should describe a principled solution, emphasize its novelty, and present an indepth evaluation of the AI techniques being exploited.

Apart from regular papers, the journal also accepts Research Notes, Research Field Reviews, Position Papers, and Book Reviews (see details below). The journal will also consider summary papers that describe challenges and competitions from various areas of AI. Such papers should motivate and describe the competition design as well as report and interpret competition results, with an emphasis on insights that are of value beyond the competition (series) itself.

From time to time, there are special issues devoted to a particular topic. Such special issues must always have open calls-for-papers. Guidance on the submission of proposals for special issues, as well as other material for authors and reviewers can be found at http://aij.ijcai.org/special-issues.

Types of Papers

Regular Papers

AIJ welcomes basic and applied papers describing mature, complete, and novel research that articulate methods for, and provide insight into artificial intelligence and the production of artificial intelligent systems. The question of whether a paper is mature, complete and novel is ultimately determined by reviewers and editors on a case-bycase basis. Generally, a paper should include a convincing motivational discussion, articulate the relevance of the research to Artificial Intelligence, clarify what is new and different, anticipate the scientific impact of the work, include all relevant proofs and/or experimental data, and provide a thorough discussion of connections with the existing literature. A prerequisite for the novelty of a paper is that the results it describes have not been previously published by other authors and have not been previously published by the same authors in any archival journal. In particular, a previous conference publication by the same authors does not disqualify a submission on the grounds of novelty. However, it is rarely the case that conference papers satisfy the completeness criterion without further elaboration. Indeed, even prize-winning papers from major conferences often undergo major revision following referee comments, before being accepted to AIJ.

AIJ caters to a broad readership. Papers that are heavily mathematical in content are welcome but should include a less technical high-level motivation and introduction that is accessible to a wide audience and explanatory commentary throughout the paper. Papers that are only purely mathematical in nature, without demonstrated applicability to artificial intelligence problems may be returned. A discussion of the work's implications on the production of artificial intelligent systems is normally expected.

There is no restriction on the length of submitted manuscripts. However, authors should note that publication of lengthy papers, typically greater than forty pages, is often significantly delayed, as the length of the paper acts as a disincentive to the reviewer to undertake the review process. Unedited theses are acceptable only in exceptional circumstances. Editing a thesis into a journal article is the author's responsibility, not the reviewers'.

Research Notes

The Research Notes section of the Journal of Artificial Intelligence will provide a forum for short communications that cannot fit within the other paper categories. The maximum length should not exceed 4500 words (typically a paper with 5 to 14 pages). Some examples of suitable Research Notes include, but are not limited to the following: crisp and highly focused technical research aimed at other specialists; a detailed exposition of a relevant theorem or an experimental result; an erratum note that addresses and revises earlier results appearing in the journal; an extension or addendum to an earlier published paper that presents additional experimental or theoretical results.

Reviews

The AIJ invests significant effort in assessing and publishing scholarly papers that provide broad and principled reviews of important existing and emerging research areas, reviews of topical and timely books related to AI, and substantial, but perhaps controversial position papers (so-called "Turing Tape" papers) that articulate scientific or social issues of interest in the AI research community.

Research Field Reviews: AIJ expects broad coverage of an established or emerging research area, and the articulation of a comprehensive framework that demonstrates the role of existing results, and synthesizes a position on the potential value and possible new research directions. A list of papers in an area, coupled with a summary of their contributions is not sufficient. Overall, a field review article must provide a scholarly overview that facilitates deeper understanding of a research area. The selection of work covered in a field article should be based on clearly stated, rational criteria that are acceptable to the respective research community within AI; it must be free from personal or idiosyncratic bias.

Research Field Reviews are by invitation only, where authors can then submit a 2-page proposal of a Research Field Review for confirmation by the special editors. The 2-page proposal should include a convincing motivational discussion, articulate the relevance of the research to artificial intelligence, clarify what is new and different from other surveys available in the literature, anticipate the scientific impact of the proposed work, and provide evidence that authors are authoritative researchers in the area of the proposed Research Field Review. Upon confirmation of the 2-page proposal, the full Invited Research Field Reviews can then be submitted and then undergoes the same review process as regular papers.

Book Reviews: We seek reviewers for books received, and suggestions for books to be reviewed. In the case of the former, the review editors solicit reviews from researchers assessed to be expert in the field of the book. In the case of the latter, the review editors can either assess the relevance of a particular suggestion, or even arrange for the refereeing of a submitted draft review.

Position Papers: The last review category, named in honour of Alan Turing as a "Turing Tapes" section of AIJ, seeks clearly written and scholarly papers on potentially controversial topics, whose authors present professional and mature positions on all variety of methodological, scientific, and social aspects of AI. Turing Tape papers typically provide more personal perspectives on important issues, with the intent to catalyze scholarly discussion.

Turing Tape papers are by invitation only, where authors can then submit a 2-page proposal of a Turing Tape paper for confirmation by the special editors. The 2-page proposal should include a convincing motivational discussion, articulate the relevance to artificial intelligence, clarify the originality of the position, and provide evidence that authors are authoritative researchers in the area on which they are expressing the position. Upon confirmation of the 2-page proposal, the full Turing Tape paper can then be submitted and then undergoes the same review process as regular papers.

Competition Papers

Competitions between AI systems are now well established (e.g. in speech and language, planning, auctions, games, to name a few). The scientific contributions associated with the systems entered in these competitions are routinely submitted as research papers to conferences and journals. However, it has been more difficult to find suitable venues for papers summarizing the objectives, results, and major innovations of a competition. For this purpose, AIJ has established the category of competition summary papers.

Competition Paper submissions should describe the competition, its criteria, why it is interesting to the AI research community, the results (including how they compare to previous rounds, if appropriate), in addition to giving a summary of the main technical contributions to the field manifested in systems participating in the competition. Papers may be supplemented by online appendices giving details of participants, problem statements, test scores, and even competition-related software.

Although Competition Papers serve as an archival record of a competition, it is critical that they make clear why the competition's problems are relevant to continued progress in the area, what progress has been made since the previous competition, if applicable, and what were the most significant technical advances reflected in the competition results. The exposition should be accessible to a broad AI audience.

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Artificial Intelligence - Journal - Elsevier

Artificial intelligence (AI) has come to define society today in ways we never anticipated. AI makes it possible for us to unlock our smartphones with our faces, ask our virtual assistants questions and receive vocalized answers, and have our unwanted emails filtered to a spam folder without ever having to address them.

These kinds of functions have become so commonplace in our daily lives that its often easy to forget that, just a decade ago, few of them existed. Yet while artificial intelligence and machine learning may have been the topic of conversation among science fiction enthusiasts since the 80s, it wasnt until much more recently that computer scientists acquired the advanced technology and the extensive amount of data needed to create the products we use today.

The impact of machine learning and AI doesnt stop at the ability to make the lives of individuals easier, however. These programs have been developed to positively impact almost every industry through the streamlining of business processes, the improving of consumer experiences, and the carrying out of tasks that have never before been possible.

This impact of AI across industries is only expected to increase as technology continues to advance and computer scientists uncover the exciting possibilities of this specialization in their field. Below, we explore what exactly artificial intelligence entails, what careers are currently defining the industry, and how you can set yourself up for success in the AI sector.

Learn how Northeasterns advanced AI degree can accelerate your career.

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The term artificial intelligence has many connotations, depending on the specific industry it is used in. Most often, however, when people say artificial intelligence, what they actually mean is machine learning, says Bethany Edmunds, associate dean and lead faculty atNortheasterns Khoury College of Computer Science. [Although AI] is a large umbrella term that incorporates a lot of statistical methods, historically, what it actually means is a computer acting like a human.

The ability of a computer to replicate human-like behavior is at the core of all AI functions. Machine learning software allows computers to witness human behavior through the intake of data. These systems then undergo advanced processes to analyze that data and identify patterns within it, using those findings to apply the discovered knowledge and replicate the behavior.

Edmunds identifies that, while advanced technology is important in this process, the key to the operation is actually the data. In fact, the astounding increase in the quantity of data collected over the last decade has had a significant impact on the advancement of the AI industry today.

Whats happening right now is that the technology has finally caught up to what people have been predicting [about AI] for a long time, she says. We finally have the right amount of data and the advanced machines that can process that data, which is why, right now, [AI] is being applied in so many sectors.

Despite the exciting opportunities that these advances are bringing to light, some individuals are still quite skeptical about the use of AI. Edmunds believes that this is due, in large part, to a lack of understanding about exactly how these processes work and the fear that comes with that.

I like to equate [the introduction of AI] to cloud computing; while people dont necessarily know how Google Drive works, they understand the concept and are faster to participate inputting their information in cloud storage, she says. AI is not like that. People dont understand the statistics behind itso it all just seems very magical.

Those who have a complex understanding of computer science and statistics, however, recognize that the potential impact of this function is endless. AI is doing amazing things today and allowing for developments across industries that weve never seen before, Edmunds says.

As the possible applications of AI continue to increase, so does the positive career potential for those with the skills needed to thrive in this industry. The World Economic Forums The Future of Jobs 2018 report predicts that there will be 58 million new jobs in artificial intelligence by 2022.

However, those with the necessary combination of skills are often hard to come by, Edmunds explains. The job market is really huge in [AI], but a lot of people arent trained for it, she says, resulting in an above-average job outlook for those who do have the skills needed to work in this niche area.

Read on to explore some of these top career areas defining the industry.

Although many of these top careers explore the application or function of AI technology, computer science and artificial intelligence research is more about discovering ways to advance the technology itself. There will always be somebody developing a faster machine, Edmunds says. Theres always going to be somebody pushing the edge, and that [person] will be a computer scientist.

Responsibilities: A computer science and artificial intelligence researchers responsibilities will vary greatly depending on their specialization or their particular role in the research field. Some may be in charge of advancing the data systems related to AI. Others might oversee the development of new software that can uncover new potential in the field. Others still may be responsible for overseeing the ethics and accountability that comes with the creation of such tools. No matter their specialization, however, individuals in these roles will work to uncover the possibilities of these technologies and then help implement changes in existing tools to reach that potential.

Career Outlook: As these individuals are at the crux of advancement in AI, their job outlook is very positive. The New York Times estimates that high-level AI researchers at top companies make more than $1,000,000 per year as of 2018, with lower-level employees making between $300,000 and $500,000 per year in both salary and stock. Individuals in base-level AI research roles are likely to make an average salary of $92,221 annually.

The AI field also relies on traditional computer science roles such as software engineers to develop the programs on which artificial intelligence tools function.

Responsibilities: Software engineers are part of the overall design and development process of digital programs or systems. In the scope of AI, individuals in these roles are responsible for developing the technical functionality of the products which utilize machine learning to carry out a variety of tasks.

Career Outlook: The Bureau of Labor Statistics predicts a growth rate of 22 percent by 2029 for software developers, including the addition of 316,000 jobs. Software engineers also make an average salary of $110,140 per year, with potential increases for those with a specialty in AI.

Many of the most popular consumer applications of AI today revolve around language. From chatbots to virtual assistants to predictive texting on smartphones, AI tools have been used to replicate human speech in a variety of formats. To do this effectively, developers call upon the knowledge of natural language processersindividuals who have both the language and technology skills needed to assist in the creation of these tools. Natural language processing is applying machine learning to language, Edmunds says. Its a really big field.

Responsibilities: As there are many applications of natural language processing, the responsibilities of the experts in this field will vary. However, in general, individuals in these roles will use their complex understanding of both language and technology to develop systems through which computers can successfully communicate with humans.

Career Outlook: Theres a real shortage of people in these roles [today], Edmunds says. There are a bunch of [products] where were trying to interact with a machine through language, but language is really hard. For this reason, those with the proper skill sets can expect an above-average salary and job outlook for the foreseeable future. The average annual salary for those with natural language processing skills is $107,641 per year.

User experience (UX) roles involve working with productsincluding those which incorporate AIto ensure that consumers understand their function and can easily use them. Although Edmunds emphasizes that these roles do exist outside of the artificial intelligence sector, the increased use of AI in technology today has led to a growing need for UX specialists that are trained in this particular area.

Responsibilities: In general, user experience specialists are in charge of understanding how humans use equipment, and thus how computer scientists can apply that understanding to the production of more advanced software. In terms of AI, a UX specialists responsibilities may include understanding how humans are interacting with these tools in order to develop functionality that better fits those humans needs down the line.

Did You Know: One of the most prominent examples of how user experience influenced technology we know today is Apple. The invention of Mac operating softwarecompared to Windowscame from the need for a product that was more user-friendly and which didnt require an advanced technical understanding to operate. Apple approached the development of the iPhone in the same way. The iPhone was all about user experience, Edmunds says. That was a [user experience expert] understanding how people interact [with their phones], including whats intuitive and whats not. Then they designed the best possible phone to fit those needs.

Job Outlook: The job outlook for user experience designers is quite positive. The average salary for UX designers is $76,440 per year (though those at the top of their field make over $100,000 annually). Job growth in this industry is expected to increase by 22.1 percent by 2022, effectively increasing opportunities for those with the right training and experience.

With data at the heart of AI and machine learning functions, those who have been trained to properly manage that data have many opportunities for success in the industry. Though data science is a broad field, Edmunds emphasizes the role that data analysts play in these AI processes as one of the most significant.

Responsibilities: Data analysts need to have a solid understanding of the data itselfincluding the practices of managing, analyzing, and storing itas well as the skills needed to effectively communicate findings through visualization. Its one thing to just have the data, but to be able to actually report on it to other people is vital, Edmunds says.

Job Outlook: Data analysts have a positive career outlook. These roles earn an average salary of $61,307 per year.

Artificial intelligence is a lucrative field with above-average job growth, but the industry remains competitive. Roles in this discipline are very niche, requiring both an advanced technical background and extensive hands-on experience. Those with this rare balance of skills and real-world exposure will be able to land any number of roles in AI and continue shaping the landscape of this constantly evolving field for years to come.

Artificial intelligence professionals share an array of practical skills and theoretical knowledge in mathematics and statistics, alongside a working understanding of role-specific tools and processes. Some AI-focused computer scientists may also pursue an understanding of the ethics and philosophy that go into giving a computer the capability to think and draw conclusions.

However, Edmunds emphasizes that, while quite advanced, these common abilities alone do not always set an individual up for a successful career in artificial intelligence. Instead, she explains, its the personal backgrounds and unique interdisciplinary skills each computer scientist brings to the table that allow them to thrive.

One of the most important factors of AI is an understanding of the application, she says. Somebody needs to look at the data [these tools use] and understand what that actually means for their specific sector.

In healthcare, for instance, an ideal AI specialist would have an understanding of data and machine learning, as well as a working knowledge of the human body. In this scenario, the specialists background in both areas allows them not only to interpret the conclusions of these AI tools, but also understand how they fit into the broader context of health.

Edmunds has also observed that, while a computer scientist with a dual background is ideal for the new kinds of applications of AI across industries, very few currently exist. If you had a dual background, you would be able to write your own check, Edmunds jokes. I can assure you, you wouldnt be looking for a job right now.

Instead of this ideal candidate, those in AI often see machine learning experts with high-level computer science and statistics abilities but without a further grasp in any particular domain. This, Edmunds identifies, is the missing piece needed for further sector-specific AI advancement.

To bridge this gap, artificial intelligence programs like those at Northeastern look to embrace students personal backgrounds or prior career paths and develop artificial intelligence specialists with the ability to make a real difference across industries.

Read More: 4 Ways Artificial Intelligence is Transforming Healthcare | AI and 3 Trends That Define the Human Resources Industry | How AI Will Transform Project Management | How Data Science is Disrupting Supply Chain Management

Those looking to either break into or advance their careers in artificial intelligence can benefit from obtaining a masters degree at a top university like Northeastern.

Those hoping to work in AI should instead consider a Master of Science in Artificial Intelligence to hone their skills, learn from top industry leaders, and obtain the real-world experience they need to properly develop a specialized career.

These practices allow Northeasterns students to prepare for their future in the changing field of artificial intelligence while always keeping the real-world aspect of their work in mind. Through experiential learning and interdisciplinary integration, [Northeasterns] masters programs are focused on developing the professional, Edmunds says. All the course work is centered around real-world problems or application domains, and we do our best to get industry practitioners in the classroom to make sure what were doing is cutting edge.

While Northeastern emphasizes the benefits of experiential learning across all of its graduate and undergraduate programs, these opportunities allow AI students specifically to practice what theyre learning in the classroom at some of the top companies in the world.

Did You Know: Northeastern has developed an array of regional campuses in locations across North America that are known for their top tech talent, including Seattle, the San Francisco Bay Area, Toronto, Charlotte, and Vancouver. These regional locations have allowed unique partnerships to develop between the university and local organizations, which happen to be among the top companies in the world. Popular co-op locations for students in these areas include Amazon, Facebook, Microsoft, Nordstrom, and Google, alongside many other leading organizations.

Northeasterns artificial intelligence program provides the rare opportunity to learn from top industry leaders, work with some of the most famous companies in the world, and develop not only relevant AI and computer science skills but those which align with your preferred specialization all before you graduate. Consider enrolling to take the first step toward a fulfilling career in the exciting artificial intelligence field.

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5 Top Careers in Artificial Intelligence - Northeastern

In 2021, Artificial Intelligence is the hottest and demanding field; most engineers want to make their career in AI, Data Science & Data Analytics. Going through the best and reliable resources is the best way to learn, So here is the list of the best AI Books.

Artificial Intelligence is the field of study that simulates the processes of human intelligence on computer systems. These processes include the acquisition of information, using them, and approximating conclusions. The research topics in AI include problem-solving, reasoning, planning, natural language, programming, and machine learning. Automation, Robotics and sophisticated computer software and programs characterize a career in Artificial Intelligence. Basic foundations in maths, technology, logic, and engineering can go a long way in kick-starting a career in Artificial Intelligence.

Here we have listed a few basic and advanced Artificial Intelligence books, which will help you find your way around AI.

By Stuart Russell and Peter Norvig

This edition covers the changes and developments in Artificial Intelligence since those covered in the last edition of this book in 2003. This book covers the latest development in AI in the field of practical speech recognition, machine translation, autonomous vehicles, and household robotics. It also covers the progress, in areas such as probabilistic reasoning, machine learning, and computer vision.

You can buy it here.

By James V Stone

In this book, key neural network learning algorithms are explained, followed by detailed mathematical analyses. Online computer programs collated from open source repositories give hands-on experience of neural networks. It is an ideal introduction to the algorithmic engines of modern-day artificial intelligence.

You can but it here.

By Denis Rothman

This book serves as a starting point for understanding how Artificial Intelligence works with the help of real-life scenarios. You will be able to understand the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks. By the end of this book, you will have understood the fundamentals of AI and worked through a number of case studies that will help you develop the business vision. This book will help you develop your adaptive thinking to solve real-life AI case. Prior experience with Python and statistical knowledge is essential to make the most out of this book.

You can buy it here.

By Chandra S.S.V

This book is primarily intended for undergraduate and postgraduate students of computer science and engineering. This textbook covers the gap between the difficult contexts of Artificial Intelligence and Machine Learning. It provides the most number of case studies and worked-out examples. In addition to Artificial Intelligence and Machine Learning, it also covers various types of learning like reinforced, supervised, unsupervised and statistical learning. It features well-explained algorithms and pseudo-codes for each topic which makes this book very useful for students.

You can buy it here.

By Tom Taulli

This book equips you with a fundamental grasp of Artificial Intelligence and its impact. It provides a non-technical introduction to important concepts such as Machine Learning, Deep Learning, Natural Language Processing, Robotics and more. Further the author expands on the questions surrounding the future impact of AI on aspects that include societal trends, ethics, governments, company structures and daily life.

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By Neil Wilkins

This book gives you a glimpse into Artificial Intelligence and a hypothetical simulation of a living brain inside a computer. This book features the following topics:

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By Deepak Khemani

This book follows a bottom-up approach exploring the basic strategies needed problem-solving mainly on the intelligence part. Its main features include an introductory course on Artificial Intelligence, a knowledge-based approach using agents all across and detailed, well-structured algorithms with proofs.

You can buy it here.

By Mariya Yao, Adelyn Zhou, Marlene Jia

Applied Artificial Intelligence is a practical guide for business leaders who are passionate about leveraging machine intelligence to enhance the productivity of their organizations and the quality of life in their communities. This book focuses on driving concrete business decisions through applications of artificial intelligence and machine language. It is one of the best practical guide for business leaders looking to get a true value from the adoption of Machine Learning Technology.

You can buy it here.

By Mahajan MD, Parag Suresh

This book explores the role of Artificial Intelligence in Healthcare, how it is revolutionizing all aspects of healthcare and guides you through the current state and future applications of AI in healthcare, including those under development. It also discusses the ethical concerns related to the use of AI in healthcare, principles of AI & how it works, the vital role of AI in all major medical specialties, & the role of start-ups and corporate players in AI in healthcare.

You can buy it here.

By Max Tegmark

This book takes its readers to the heart of the latest AI thought process to explore the next phase of human existence. The author here explores the burning questions of how to prosper through automation without leaving people jobless, how to ensure that future AI systems work as intended without malfunctioning or getting hacked and how to flourish life with AI without eventually getting outsmarted by lethal autonomous machines.

You can buy it here.

By Dr. Dheeraj Mehrotra This book delivers an understanding of Artificial Intelligence and Machine Learning with a better framework of technology.

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By Peter Norvig

This book teaches advanced Common Lisp techniques in the context of building major AI systems. It reconstructs authentic, complex AI programs using state-of-the-art Common Lisp, builds and debugs robust practical programs while demonstrating superior programming style and important AI concepts. It is a useful supplement for general AI courses and an indispensable reference for a professional programmer.

You can buy it here.

By Rahul Kumar, Ankit Dixit, Denis Rothman, Amir Ziai, Mathew Lamons

This book helps you to gain real-world contextualization using deep learning problems concerning research and application. Design and implement machine intelligence using real-world AI-based examples. This book offers knowledge in machine learning, deep learning, data analysis, TensorFlow, Python, fundamentals of AI and will be able to apply your skills in real-world projects.

You can buy it here.

By Giuseppe Bonaccorso, Armando Fandango, Rajalingappaa Shanmugamani

This book is a complete guide to learning popular machine learning algorithms. You will learn how to extract features from your dataset and perform dimensionality reduction by using Python-based libraries. Then you will be learning the advanced features of Tensorflow and implement different techniques related to object classification, object detection, image segmentation and more. By the end of this book, you will have an in-depth knowledge of Tensorflow and will be the go-to person for solving AI problems.

You can buy it here.

By Chris Baker

This book explores the potential consequences of Artificial Intelligence and how it will shape the world in the coming years. It familiarizes how AI aims to aid human cognitive limitations. It covers:

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By John Mueller and Luca Massaron

This offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. This book makes it easy to understand and implement machine learning seamlessly. It explains how

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By Ethem Alpaydin

It is a concise overview of machine learning which underlies applications that include recommendation systems, face recognition, and driverless cars. The author offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.

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By John D. Kelleher, Brian Mac Namee

It is a comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution.

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By Chris Sebastian

This book traces the development of Machine Learning from the early days of computer learning to machines being able to beat human experts. It explains the importance of data and how massive amounts of it provide ML programmers with the information they need to developing learning algorithms. This book explores the relationship between Artificial Intelligence and Machine Learning.

You can buy it here.

By Deepti Gupta

It is a Data Science Bool with an effective understanding on ML Algorithms on R and SAS. This book provides real-time industrial data sets. It covers the Role of Analytics in various Industries with case studies in Banking, Retail, Telecommunications, Healthcare, Airlines and FMCG along with Analytical Solutions.

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By Lopez de Prado, Marcos

This book teaches readers how to structure Big Data in a way that is amenable to Machine Language Algorithms, how to conduct research on that data with ML algorithms, how to use supercomputing methods and how to backtest discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis and explains scientifically sound solutions using math, supported by code and examples.

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By Stuart Russel

In this book, the author explores the idea of intelligence in humans and machines. He describes the near time benefits that can be expected from intelligent personal assistants to vastly accelerated scientific researches. The author suggests that AI can be built on a new foundation by which machines will be designed where they will be uncertain about the human preference they are required to satisfy. Such machines would be humble, altruistic and committed to pursuing human objectives.

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A career in Artificial Intelligence can be realized in a variety of spheres which include private organizations, public undertakings, education, arts, health care, government services, and military. The extent of artificial intelligence continues to advance every day. Hence, those with the ability to translate those digital bits of data into meaningful human conclusions will be able to sustain a much rewarding career in this field. You can check out a lot many courses and certifications provided online in this field. If your intent is promising, the courses will definitely be promising and a whole lot of opportunities will show up on your way.

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Top 10 Artificial Intelligence Books for Beginner in 2021 ...