Mediaboss Marketing

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.

You can buy it here.

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:

You can buy it here.

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.

You can buy it here.

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:

You can buy it here.

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

You can buy it here.

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.

You can buy it here.

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.

You can buy it here.

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.

You can buy it here.

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.

You can buy it here.

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.

You can buy it here.

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.

People are also reading:

View post:
Top 10 Artificial Intelligence Books for Beginner in 2021 ...

How should IT leaders and professionals go about selecting and delivering the technology required to deliver the storied marvels of artificial intelligence and machine learning? AI and ML require having many moving parts in their right places, moving in the right direction, to deliver on the promise these technologies bring -- ecosystems, data, platforms, and last, but not least, people.

Is there a way for IT leaders to be proactive about AI and ML without ruffling and rattling an organization of people who want the miracles of AI and ML delivered tomorrow morning? The answer is yes.

The authors of a recent report from MIT Sloan Management Review and SAS advocates a relatively new methodology to successfully accomplish the delivery AI and ML to enterprises called "ModelOps." While there a lot of "xOps" now entering our lexicon, such as MLOps orAIOps, ModelOps is more "mindset than a specific set of tools or processes, focusing on effective operationalization of all types of AI and decision models."

That's because in AI and ML, models are the heart of the matter, the mechanisms that dictate the assembly of the algorithms, and assure continued business value. ModelOps, which is short for :model operationalization, "focuses on model life cycle and governance; intended to expedite the journey from development to deployment -- in this case, moving AI models from the data science lab to the IT organization as quickly and effectively as possible."

In terms of operationalizing AI and ML, "a lot falls back on IT," according to Iain Brown, head of data science for SAS, U.K. and Ireland, who is quoted in the report. "You have data scientists who are building great innovative things. But unless they can be deployed in the ecosystem or the infrastructure that exists -- and typically that involves IT - - there's no point in doing it. The data science community and AI teams should be working very closely with IT and the business, being the conduit to join the two so there's a clear idea and definition of the problem that's being faced, a clear route to production. Without that, you're going to have disjointed processes and issues with value generation."

ModelOps is a way to help IT leaders bridge that gap between analytics and production teams, making AI and ML-driven lifecycle "repeatable and sustainable," the MIT-SAS report states. It's a step above MLOps or AIOps, which "have a more narrow focus on machine learning and AI operationalization, respectively," ModelOps focuses on delivery and sustainability of predictive analytics models, which are the core of AI and ML's value to the business. ModelOps can make a difference, the report's authors continue, "because without it, your AI projects are much more likely to fail completely or take longer than you'd like to launch. Only about half of all models ever make it to production, and of those that do, about 90% take three months or longer to deploy."

Getting to ModelOps to manage AI and ML involves IT leaders and professionals pulling together four key elements of the business value equation, as outlined by the report's authors.

Ecosystems: These days, every successful technology endeavor requires connectivity and network power. "An AI-ready ecosystem should be as open as possible, the report states. "Such ecosystems don't just evolve naturally. Any company hoping to use an ecosystem successfully must develop next-generation integration architecture to support it and enforce open standards that can be easily adopted by external parties."

Data:Get to know what data is important to the effort. "Validate its availability for training and production. Tag and label data for future usage, even if you're not sure yet what that usage might be. Over time, you'll create an enterprise inventory that will help future projects run faster."

Platforms: Flexibility and modularity -- the ability to swap out pieces as circumstance change -- is key. The report's authors advocate buying over building, as many providers have already worked out the details in building and deploying AI and ML models. "Determine your cloud strategy. Will you go all in with one cloud service provider? Or will you use different CSPs for different initiatives? Or will you take a hybrid approach, with some workloads running on-premises and some with a CSP? : Some major CSPs typically offer more than just scalability and storage space, such as providing tools and libraries to help build algorithms and assisting with deploying models into production."

People: Collaboration is the key to successful AI and ML delivery, but it's also important that people have a sense of ownership over their parts of the projects."Who owns the AI software and hardware - the AI team or the IT team, or both? This is where you get organizational boundaries that need to be clearly defined, clearly understood, and coordinated." Along with data scientists, a group that is just as important to ModelOps is data engineers, who bring "significant expertise in using analytics and business intelligence tools, database software, and the SQL data language, as well as the ability to consistently produce clean, high-quality, ethical data."

Original post:
At last, a way to build artificial intelligence with business results in mind: ModelOps - ZDNet

Reflation trade has been pummelled after the Federal Reserve unexpectedly signalled a shift in its stance on inflation, and, European Central Bank executive Fabio Panetta says the introduction of a digital euro would boost consumers privacy. Plus, the FTs innovation editor, John Thornhill, talks about the new season of the Tech Tonic podcast and its main focus, artificial intelligence.

Reflation trades pummelled as Fed shift resets markets

https://www.ft.com/content/2fa0c907-f597-49b2-a08d-35249d1d5a9f

Digital euro will protect consumer privacy, ECB executive pledges

https://www.ft.com/content/e59e5d61-043a-4293-8692-f8267e5984c2?

Tech Tonic Season 2

https://www.ft.com/tech-tonic

Today's Clubhouse discussion on artificial intelligence

https://www.clubhouse.com/join/FinancialTimes/MLICXXgQ/PAwJ017M

See acast.com/privacy for privacy and opt-out information.

A transcript for this podcast is currently unavailable, view our accessibility guide.

Go here to see the original:
How artificial intelligence is reshaping the world - Financial Times

Search for Extraterrestrial Intelligence (SETI) astronomer Seth Shostak (pictured) confesses that these are exciting times for alien hunters like himself, what with the Pentagons anticipated July 25 report on unidentified aerial phenomena. Still, he doesnt expect any big revelations: I think its overwhelmingly likely that aliens are present in our galaxy. But I dont believe theyre hanging out in our airspace. Not now, and not in historic times.

On the other hand, he goes on to say, every third star in our galaxy could host an Earth-like planet so the odds are we are not alone. Few life forms on Earth resemble humans, so why should extraterrestrials?

But if we are not alone, what would ET be like? A gaseous cloud? A plant? Pure information?

Shostak argues, somewhat daringly, that ETs who traveled to Earth would probably not be alive in a conventional sense. In fact, given the immense interstellar distances, not being alive might be the only way they could get here. They might be artificial intelligences:

Such leisurely trips arent going to appeal to biological passengers who will die long before their destination is reached. Machines, on the other hand, wont complain if theyre cooped up in a spaceship for tens of thousands of years. They dont require food, oxygen, sanitation or entertainment. And they dont insist on a round-trip ticket.

Artificial intelligence aliens may not be as appealing as those who are warm-blooded and squishy, but we shouldnt get hung up on an anthropocentric viewpoint. Researchers who work in AI estimate that machines able to beat humans on an IQ test will emerge from the labs by mid-century. If we can do it, some extraterrestrials will have already done it.

Shostak sounds overly optimistic about artificial intelligence beating humans on an IQ test. Its helpful to remember that computers only compute. Many thought processes are not forms of computing. As tech philosopher George Gilder points out, AI can win if the map is the territory (think chess) because then pure computation can win. But a map of Earth is not the territory and non-computational methods of thought are essential.

Of greater concern would be its intentions. Most sci-fi stories postulate that visitors would be noxious, arriving with a primal urge to obliterate Los Angeles or London. Frankly, if thats whats on their mechanical minds, its probably impossible to keep them at bay. Chimps couldnt outsmart humans in any serious confrontation. Likewise, devices who can manage a trip to Earth will have the capability to do whatever they wish once they get here.

But wait. If they are artificial intelligences, they wouldnt have any desires at all. Someone might have programmed them to do thus-and-such. But obliterating the newly discovered research subjects, after all that time and trouble, seems like an unlikely program.

Waiting for the Pentagons report is a fun time and we are all entitled to our speculations.

You may also wish to read: Astronomer bets a cup of coffee that well encounter ET by 2036 Seth Shostak points to the increase in the number of exoplanets identified and the increase in computing power. One problem is that signals to and from exoplanets may take years. It takes up to 21 minutes for a signal from Earth to reach even Mars.

and

Seven reasons (so far) why the aliens never show up. Some experts think they became AI and some that they were killed by their AI but others say they never existed. Whos most likely right? Indeed, where are they? A flurry of explanations creates some great sci-fi.

View original post here:
ET Expert Thinks That ET Is Probably AI (Artificial Intelligence) - Walter Bradley Center for Natural and Artificial Intelligence

AI, Artificial Intelligence concept,3d rendering,conceptual image.

The Biden administration has recently set into action its initiative on AI (Artificial Intelligence).This is part of legislation that was passed last year and included a budget of $250 million (for a period of five years).The goals are to provide easier access to the troves of government data as well as provide for advanced systems to create AI models.

No doubt, this effort is a clear sign of the strategic importance of the technology.It is also a recognition that the U.S. does not want to fall behind other nations, especially China.

The AI task force has 12 distinguished members who are from government, private industry and academia.This diversity should help provide for a smarter approach.

But the focus on data will also be critical. In areas of social importance such as housing, healthcare, education or other social determinants, the government is the only central organizer of data, said Dr. Trishan Panch, who is the co-founder of Wellframe.As such, if AI is going to deliver gains in these areas, the government has to be involved.

Yet there will certainly be challenges.Lets face it, the U.S. government often moves slowly and is burdened with various levels of local, state and federal authorities.

To achieve the initiatives vision, government entities will need to go beyond sharing best practices and figure out how to share more data across departments, said Justin Borgman, who is the CEO of Starburst.For instance, expanding open data initiatives which today are largely siloed by departments, would greatly improve access to data. That would give Artificial Intelligence systems more fuel to do their jobs.

If anything, there will be a need for a different mindset from the government.And this could be a heavy lift.Based on my experience in the public sector, the major challenge for the government is addressing the Missing Middle, said Jon Knisley, who is the Principal of Automation and Process Excellence at FortressIQ. There are a number of very advanced programs on one end, and then there are a lot of emerging programs on the other end. The greatest opportunity lies in closing that gap and driving more adoption. To be successful, there should be a focus as much as possible on applied AI.

But the government initiative can do something that has been difficult for the private sector to achievethat is, to help reskill the workforce for AI.This is perhaps one of the biggest challenges for the U.S.

The question is: How do we create a large AI data science force that is integrated across every industry and department in the US?, said Judy Lenane, who is the Chief Medical Officer at iRhythm.To start, well need to begin AI curriculum early and encourage its growth in order to build a comprehensive workforce. This will be especially critical for industries that are currently behind in technological adoption, such as construction and infrastructure, but it also needs to be accessible.

In the meantime, the Biden AI effort will need to deal with the complex issues of privacy and ethics.

Presently there is significant resistance on this subject given that most consumers feel that their privacy has been compromised, said Alice Jacobs, who is the CEO of convrg.ai.This is the result of a lack of transparency around managing consents and proper safeguards to ensure that data is secure. We will only be able to be successful if we can manage consents in a way where the consumer feels in control of their data.Transparent unified consent management will be the path forward to alleviate resistance around data access and can provide the US a competitive advantage in this data and AI arms race.

Tom (@ttaulli) is an advisor/board member to startups and the author of Artificial Intelligence Basics: A Non-Technical Introduction, The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems and Implementing AI Systems: Transform Your Business in 6 Steps. He also has developed various online courses, such as for the COBOL and Python programming languages.

More here:
Bidens AI Initiative: Will It Work? - Forbes