Archive for the ‘Artificial Intelligence’ Category

5 Top Careers in Artificial Intelligence – Northeastern

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.

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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

Top 10 Artificial Intelligence Books for Beginner in 2021 …

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.

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

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."

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

How artificial intelligence is reshaping the world – Financial Times

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

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How artificial intelligence is reshaping the world - Financial Times

ET Expert Thinks That ET Is Probably AI (Artificial Intelligence) – Walter Bradley Center for Natural and Artificial Intelligence

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.

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ET Expert Thinks That ET Is Probably AI (Artificial Intelligence) - Walter Bradley Center for Natural and Artificial Intelligence