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Life 3.0: Being Human in the Age of Artificial Intelligence (英語) ハードカバー – ラフカット, 2017/8/29

Enjoy the ride, and you will come out the other end with a greater appreciation of where people might take technology and themselves in the years ahead.” —Science“This is a compelling guide to the challenges and choices in our quest for a great future of life, intelligence and consciousness—on Earth and beyond.”

The jury is out, but this enlightening, lively and accessible book by a distinguished scientist helps us to assess the odds.' —Professor Martin Rees, Astronomer Royal, cosmology pioneer, author of Our Final Hour 'In [Tegmark's] magnificent brain, each fact or idea appears to slip neatly into its appointed place like another little silver globe in an orrery the size of the universe.

In the meantime, he has forged a remarkable consensus on the need for AI researchers to work on the mind-bogglingly complex task of building digital chains that are strong and durable enough to hold a superintelligent machine to our bidding....This is a rich and visionary book and everyone should read it.' —The Times (UK)'Life 3.0 is far from the last word on AI and the future, but it provides a fascinating glimpse of the hard thinking required.'

But the idea that machine-based superintelligence could somehow run amok is fiercely resisted by many computer scientists....Yet the notion enjoys more credence today than a few years ago, partly thanks to Mr. Tegmark.” —Wall Street Journal 'Tegmark’s book, along with Nick Bostrom’s Superintelligence, stands out among the current books about our possible AI futures....Tegmark explains brilliantly many concepts in fields from computing to cosmology, writes with intellectual modesty and subtlety, does the reader the important service of defining his terms clearly, and rightly pays homage to the creative minds of science-fiction writers who were, of course, addressing these kinds of questions more than half a century ago.

It’s often very funny, too.' —The Telegraph (UK)“Exhilarating….MIT physicist Tegmark surveys advances in artificial intelligence such as self-driving cars and Jeopardy-winning software, but focuses on the looming prospect of “recursive self-improvement”—AI systems that build smarter versions of themselves at an accelerating pace until their intellects surpass ours.

What is Artificial Intelligence Marketing?

Artificial Intelligence (AI) has made the transition from once being a glorious manifestation of sci-fi imagination to today emerging as a technological reality capable of disrupting industries.

It is the calibrated use of customer data — from online and offline sources — and computational concepts such as Machine Learning to predict customers’ digital actions or inactions (on web or mobile app platforms), enabling businesses to intelligently target the right customers with the right content across the right channel, and at just the right time.

These segments can be then be targeted with laser-focused personalised and contextualised content through appropriate channels of communication, such as emails, browser push notifications, app push notifications, or in-app messages, to nudge them along their journey towards conversion.

AI further allows marketers to easily embed high-sentiment keywords in these messages that have resulted in a conversion event based on key metrics such as open and click rates relevant to each customer, chronicled over a period of time.

For instance, a media OTT app will recommend a horror film to a user based on his genre consumption history, either through an in-app message (during an active session) or through a triggered email when the film is added to the content library.

For instance, a Chatbot integrated on a food delivery provider’s platform can be used to render 24-hour live chat customer support based on FAQs pertaining to delivery tracking, order cancellation, refund process, etc.

With the proliferation of voice assistants and emergence of IoT, brands that carefully evaluate and embrace the power of AI in an attempt to create an integrated, seamless, and personalised customer experience are more likely to reap the rewards of higher ROI, customer engagement, retention, and top-line growth.

Applications of artificial intelligence

Artificial intelligence, defined as intelligence exhibited by machines, has many applications in today's society.

where programs are developed to perform specific tasks, that is being utilized for a wide range of activities including medical diagnosis, electronic trading, robot control, and remote sensing.

The AOD has use for artificial intelligence for surrogate operators for combat and training simulators, mission management aids, support systems for tactical decision making, and post processing of the simulator data into symbolic summaries.[2]

The artificial intelligent programs can sort the information and provide the pilot with the best possible maneuvers, not to mention getting rid of certain maneuvers that would be impossible for a human being to perform.

Haitham Baomar and Peter Bentley are leading a team from the University College of London to develop an artificial intelligence based Intelligent Autopilot System (IAS) designed to teach an autopilot system to behave like a highly experienced pilot who is faced with an emergency situation such as severe weather, turbulence, or system failure.[7]

Educating the autopilot relies on the concept of supervised machine learning “which treats the young autopilot as a human apprentice going to a flying school”.[7]

The Intelligent Autopilot System combines the principles of Apprenticeship Learning and Behavioural Cloning whereby the autopilot observes the low-level actions required to maneuver the airplane and high-level strategy used to apply those actions.[8]

There are a number of companies that create robots to teach subjects to children ranging from biology to computer science, though such tools have not become widespread yet.

Universities have been slow in adopting AI technologies due to either a lack of funding or skepticism of the effectiveness of these tools, but in the coming years more classrooms will be utilizing technologies such as ITS to complement teachers.

Advancements in natural language processing, combined with machine learning, have also enabled automatic grading of assignments as well as a data-driven understanding of individual students’ learning needs.

Data sets collected from these large scale online learning systems have also enabled learning analytics, which will be used to improve the quality of learning at scale.

Examples of how learning analytics can be used to improve the quality of learning include predicting which students are at risk of failure and analyzing student engagement.[11]

Algorithmic trading involves the use of complex AI systems to make trading decisions at speeds several orders of magnitudes greater than any human is capable of, often making millions of trades in a day without any human intervention.

Automated trading systems are typically used by large institutional investors, but recent years have also seen an influx of smaller, proprietary firms trading with their own AI systems.[12]

Its wide range of functionalities includes the use of natural language processing to read text such as news, broker reports, and social media feeds.

For example, Digit is an app powered by artificial intelligence that automatically helps consumers optimize their spending and savings based on their own personal habits and goals.

The app can analyze factors such as monthly income, current balance, and spending habits, then make its own decisions and transfer money to the savings account.[16]

Wallet.AI, an upcoming startup in San Francisco, builds agents that analyze data that a consumer would leave behind, from Smartphone check-ins to tweets, to inform the consumer about their spending behavior.[17]

This class of financial advisers work based on algorithms built to automatically develop a financial portfolio according to the investment goals and risk tolerance of the clients.

An online lender, Upstart, analyze vast amounts of consumer data and utilizes machine learning algorithms to develop credit risk models that predict a consumer’s likelihood of default.

This platform utilizes machine learning to analyze tens of thousands traditional and nontraditional variables (from purchase transactions to how a customer fills out a form) used in the credit industry to score borrowers.

In a paper by Fivos Papadimitriou (2012), he describes a system written in Prolog which can be used to provide the user with information about the transformations of Mediterranean-type landscapes in an interactive way, allow the modelling of causes and effects of landscape transformations (such as land degradation) and forecast future landscape changes.[21]

AI-powered engine streamlines the complexity of job hunting by operating information on job skills, salaries, and user tendencies, matching people to the most relevant positions.

Machine intelligence calculates what wages would be appropriate for a particular job, pulls and highlights resume information for recruiters using natural language processing, which extracts relevant words and phrases from text using specialized software.

Robots have proven effective in jobs that are very repetitive which may lead to mistakes or accidents due to a lapse in concentration and other jobs which humans may find degrading.

In the automotive industry, a sector with particularly high degree of automation, Japan had the highest density of industrial robots in the world: 1,414 per 10,000 employees.[24]

Recruiting with AI also produced Unililever’s “most diverse class to date.’ Unilever also decreased time to hire from 4 months to 4 weeks and saved over 50,000 hours of recruiter time.

Ari automates posting jobs, advertising openings, screening candidates, scheduling interviews, and nurturing candidate relationships with updates as they progress along the hiring funnel.

Typical use case scenarios include the analysis of images using object recognition or face recognition techniques, or the analysis of video for recognizing relevant scenes, objects or faces.

The motivation for using AI-based media analysis can be — among other things — the facilitation of media search, the creation of a set of descriptive keywords for a media item, media content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), speech to text for archival or other purposes, and the detection of logos, products or celebrity faces for the placement of relevant advertisements.

Another artificial intelligence musical composition project, The Watson Beat, written by IBM Research, doesn't need a huge database of music like the Google Magenta and Flow Machines projects, since it uses Reinforcement Learning and Deep Belief Networks to compose music on a simple seed input melody and a select style.

The company Narrative Science makes computer generated news and reports commercially available, including summarizing team sporting events based on statistical data from the game in English.

Yseop is able to write financial reports, executive summaries, personalized sales or marketing documents and more at a speed of thousands of pages per second and in multiple languages including English, Spanish, French &

Boomtrain’s is another example of AI that is designed to learn how to best engage each individual reader with the exact articles — sent through the right channel at the right time — that will be most relevant to the reader.

The program would start with a set of characters who wanted to achieve certain goals, with the story as a narration of the characters’ attempts at executing plans to satisfy these goals.[47]

Their particular implementation was able faithfully reproduced text variety and complexity of a number of stories, such as red riding hood, with human-like adroitness.[49]

This prospered greatly with the Digital Revolution, and helped introduce people, especially children, to a life of dealing with various types of Artificial Intelligence, specifically in the form of Tamagotchis and Giga Pets, iPod Touch, the Internet, and the first widely released robot, Furby.

The major challenge to developing this AI is the fact that transportation systems are inherently complex systems involving a very large number of components and different parties, each having different and often conflicting objectives.[58].

Applications are also being developed for gesture recognition (understanding of sign language by machines), individual voice recognition, global voice recognition (from a variety of people in a noisy room), facial expression recognition for interpretation of emotion and non verbal cues.

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