Topic 26: Artificial intelligence

Most researchers agree that artificial intelligence (AI) peaked around 1985. A public (1) _______ science-fiction movies and excited by the growing power of computers had high expectations. For years, Al researchers had implied that a breakthrough was just (2) ____ the corner. Marvin Minsky said in 1967 that within a generation the problem of creating AI would be (3) ____ solved. Prototypes of medical-diagnosis programs and speech recognition software appeared to be making progress. It proved to be a false dawn. Thinking computers and household robots failed to (4) ______, and a backlash ensued. "There was undue optimism in the early 1980s’, says David Leaky, a researcher at Indiana University. “Then when people realised these were hard problems, there was retrenchment’. By the late 1980s, the term AI was being avoided by many researchers, who opted instead (5) ____ themselves with specific sub-disciplines such as neural networks, agent technology, case-based reasoning.

A scientist said robots will be more (1) ____ than humans by 2029. The scientist’s name is Ray Kurzweil. He works for Google as Director of Engineering. He is one of the world’s leading experts on artificial intelligence (A.I.). Mr Kurzweil believes computers will be able to learn from experiences, just like humans. He also thinks they will be able to (2) ____ jokes and stories, and even flirt. Kurzweil’s 2029 prediction is a lot sooner than many people thought. The scientist said that in 1999, many A.I. experts said it would be hundreds of years before a computer was more intelligent than a human. He said that it would not be (3) ____ before computer intelligence is one billion times more powerful than the human brain.

Mr Kurzweil joked that many years ago, people thought he was a little crazy for predicting computers would be as intelligent as humans. His thinking has stayed the same but everyone else has changed the way they think. He said: “My views are not radical any more. I’ve actually stayed consistent. It’s the rest of the world that’s changing its view.” He highlighted examples of (4) ____ -tech things we use, see or read about every day. These things make us believe that computers have intelligence. He said people think (5) ____ now: “Because the public has seen things like Siri (the iPhone’s voice recognition technology) where you talk to a computer, they’ve seen the Google self-driving cars.”

Our eyes are the window to our soul, so the saying goes, but they’re also a window into our health. Picking up eye problems early can significantly (1) ____ the chance of sight loss.

Several programs are looking at how to combine existing medical knowledge about our eyes with AI (Artificial Intelligence) tools.

Google DeepMind has teamed up with Moorfields Eye Hospital in London to work on (2) ____ two major conditions that cause sight loss: diabetic retinopathy and age-related macular (3) ____ (AMD). Together, these eye diseases affect more than 625,000 people in the UK and over 100 million people worldwide.

Algorithms have been trained using thousands of eye scans, then set to work detecting potential issues, allowing doctors (4) ____ the right course of action in a fraction of the time it would normally take and with a greater degree of certainty. DeepMind says that 300,000 UK patients a year could be helped (5) ____ the system is given the go ahead for general use following the completion of clinical trials.

We’re not surprised if you haven’t been following the recent developments in AI all that closely because, for the most part, it’s seemed like nothing exciting has happened for quite a long time. Sci-fi dreams about computer powered best friends aside, AI for the general public has come to mean reasonably responsive and well-programmed computer assistance rather than independent thinking machines. Concepts like ‘smart’ chatbots somehow seem to pull us further from the Star Trek or Heinlinian dream of fully sentient and intuitive computers while many products and services that claim to integrate AI seem to be nothing more than a fast way to analyze large amounts of data.

In fact, the last time most of us heard something hopeful about AI was when Deep Blue beat the world Chess champion, but what ever came of that AI? Surely it hasn’t used that incredible logical power to take over the world or begin making friends, so what do we even care?

While practical applications for specifically built AI are growing, the tradition of training your AI programming skills on classic strategy games has existed since the 1950s when a computer was programmed to play and was able to win a game of tic-tac-toe. Since then a large variety of games and custom-built AIs have been tested against each other to the great entertainment of experts in the field and curious nerds like us who care about that sort of thing. The real difference is not what they’re programmed for but how they are programmed to start with and, in fact, this is also what most profoundly distinguishes AlphaGo from its older-generation relative, the Chess champion DeepBlue.

(Source: https://medium.com)

Most of the roughly 1,400 active volcanoes around the world, including many in the United States, do not have on-site observatories. Lacking ground-level data, scientists are turning to satellites to keep tabs on volcanoes from space. Now using artificial intelligence, scientists have created a new satellite-based method of detecting warning signs of when a volcano is likely to erupt.

Every time one of the satellites passes over a given volcano, it can capture an InSAR image of the volcano from which ground movement away from or toward the satellite can be calculated.

InSAR can often pick up the ominous expansion of the ground that occurs when magma moves within a volcano’s plumbing, but it is difficult to continuously monitor the huge number of images produced by the latest generation of SAR-equipped satellites. In addition, some volcanoes exhibit long-lasting deformation that poses no immediate threat, and new images must be compared with older ones to determine whether a deformation at a volcano is a warning sign or just business as usual. To solve these issues, the researchers turned to machine learning, a form of artificial intelligence that can glean subtle patterns in vast quantities of data. They developed an algorithm that can rapidly analyze InSAR data, compare current deformation to past activity, and automatically create an alert when a volcano’s unrest may be cause for concern.

To test the algorithm’s viability, the team applied it to real data from the period leading up to the 2018 eruption of Sierra Negra, a volcano in the Galápagos Islands. The algorithm worked, flagging an increase in the ground’s inflation that began about a year before the eruption. Had the method been available at the time, the team writes, it would have accurately alerted researchers that Sierra Negra was likely to erupt.

(Source: https://eos.org/)

Automated manufacture arose out of the intimate relationship of such economic forces and technical innovations as the division of labor, power transfer and the mechanization of the factory, and the development of transfer machines and feedback systems as explained below.

The division of labor (that is, the reduction of a manufacturing or service process into its smallest independent steps) developed in the latter half of the 18th century and was first discussed by the Scottish economist Adam Smith in his book An Inquiry into the Nature and Causes of the Wealth of Nations (1776). In manufacturing, the division of labor results in increased production and a reduction in the level of skills required of workers.

Mechanization was the next step necessary in the development of automation. The simplification of work made possible by the division of labor also made it possible to design and build machines that duplicated the motions of the worker. As the technology of power transfer evolved, these specialized machines were motorized and their production efficiency was improved. The development of power technology also gave rise to the factory system of production, because all workers and machines had to be located near the power source.

The transfer machine is a device used to move a workpiece from one specialized machine tool to another, in such a manner as to properly position the workpiece for the next machining operation. Industrial robots, originally designed only to perform simple tasks in environments dangerous to human workers, are now extremely dexterous and are being used to transfer, handle, and index (that is, to position) both light and heavy workpieces, thus performing all the functions of a transfer machine. In actual practice, a number of separate machines are integrated into what may be thought of as one large machine.

In the 1920s the auto industry combined these concepts into an integrated system of production. The goal of this assembly-line system was to make automobiles available to people who previously could not afford them. This method of production was adopted by most automobile manufacturers and rapidly became known as Detroit automation. Despite more recent advances, it is this system of production that most people think of as automation.

The aviation industry, especially the commercial aviation sector, is constantly striving to improve both the way it works and its customer satisfaction. It has begun using artificial intelligence. Though AI in the aviation industry is still in the nascent stage, some progress has been made already as certain leading carriers invest in AI. To make a long story short, AI can redefine how the aviation industry goes about its work.

In 2017, American Airlines conducted an app development competition with the goal of having an app developed for making baggage screening easier for passengers. The competition, named HackWars, was themed upon artificial intelligence, drones and augmented and virtual reality. The winner, known as “Team Avatar,” developed an app that would not only allow passengers determine their baggage size before arriving at the airport, but also prepay any potential baggage-related expenses.

United Airlines is using Amazon’s Alexa to have certain common customer queries answered. In September 2017, United announced a collaboration with Alexa. The feature is known as the United skill. To get started, all passengers need to do is to add the United skill to their Alexa app and then start asking questions. Alexa answers common queries correctly, such as the status of a flight by number, check-in requests and availability of Wi-Fi on a flight. The reviews so far have been mixed, which points to the fact that there is a learning curve, and it is still a long way to go before AI can fully handle customer assistance. Tracking progress is an enormous challenge that airlines will face. The first thing they need to do is to develop analytics that will help them develop and process accurate data.

However, that in itself is a challenge. What kind of analytics will help? For example, customer satisfaction is going to be one of the most important factors in success. What kind of analytics will determine that airlines have been improving on customer satisfaction parameters?

AI needs huge investments, and probably the biggest risk in this is smaller, especially budget airlines are going to miss out on reaping the benefits of AI fully. Does that mean that the performance of the smaller carriers will be impacted? That might not be the case, because we might be moving toward more acquisitions and mergers. Bigger airlines will have a massive appetite for acquiring smaller airlines with an eye on the market. It is not all gloom and doom though, because smaller airlines like Southwest have already shown some initiatives toward embracing AI.

It is surprising that a sector as important as aviation has woken up to AI so late. As AI in aviation picks up its pace, there could probably be a few mergers, acquisitions or even closure of small airlines which will not be able to afford the investments. Now, AI seems the best option to take aviation to the next level.

(Source: https://www.techopedia.com/)

Like the revolutions that preceded it, the Fourth Industrial Revolution has the potential to raise global income levels and improve the quality of life for populations around the world. To date, those who have gained the most from it have been consumers able to afford and access the digital world; technology has made possible new products and services that increase the efficiency and pleasure of our personal lives. Ordering a cab, booking a flight, buying a product, making a payment, listening to music, watching a film or playing a game — any of these can now be done remotely.

In the future, technological innovation will also lead to a supply-side miracle, with long-term gains in efficiency and productivity. Transportation and communication costs will drop, logistics and global supply chains will become more effective and the cost of trade will diminish, all of which will open new markets and drive economic growth.

At the same time, as the economists Erik Brynjolfsson and Andrew McAfee have pointed out, the revolution could yield greater inequality, particularly in its potential to disrupt labor markets. As automation substitutes for labor across the entire economy, the net displacement of workers by machines might exacerbate the gap between returns to capital and returns to labor. On the other hand, it is also possible that the displacement of workers by technology will, in aggregate, result in a net increase in safe and rewarding jobs. We cannot foresee at this point which scenario is likely to emerge, and history suggests that the outcome is likely to be some combination of the two. However, I am convinced of one thing — that in the future, talent, more than capital, will represent the critical factor of production. This will give rise to a job market increasingly segregated into “low-skill/low-pay” and “high-skill/high-pay” segments, which in turn will lead to an increase in social tensions.

In addition to being a key economic concern, inequality represents the greatest societal concern associated with the Fourth Industrial Revolution. The largest beneficiaries of innovation tend to be the providers of intellectual and physical capital — the innovators, shareholders and investors — which explains the rising gap in wealth between those dependent on capital versus labor. Technology is therefore one of the main reasons why incomes have stagnated, or even decreased, for a majority of the population in high-income countries: the demand for highly skilled workers has increased while the demand for workers with less education and lower skills has decreased. The result is a job market with a strong demand at the high and low ends, but a hollowing out of the middle. This helps explain why so many workers are disillusioned and fearful that their own real incomes and those of their children will continue to stagnate. It also helps explain why middle classes around the world are increasingly experiencing a pervasive sense of dissatisfaction and unfairness. A winner-takes-all economy that offers only limited access to the middle class is a recipe for democratic malaise and dereliction.

(Source: https://www.ge.com/)

Many AI researchers roll their eyes when seeing this headline: “Stephen Hawking warns that rise of robots may be disastrous for mankind.” And as many have lost count of how many similar articles they’ve seen. Typically, these articles suggest we should worry about robots rising up and killing us because they’ve become conscious and/or evil. On a lighter note, such articles are actually rather impressive, because they succinctly summarize the scenario that AI researchers don’t worry about. That scenario combines as many as three separate misconceptions: concern about consciousness, evil, and robots.

If you drive down the road, you have a subjective experience of colors, sounds, etc. But does a self-driving car have a subjective experience? Does it feel like anything at all to be a self-driving car? Although this mystery of consciousness is interesting in its own right, it’s irrelevant to AI risk. If you get struck by a driverless car, it makes no difference to you whether it subjectively feels conscious. In the same way, what will affect us humans is what superintelligent AI does, not how it subjectively feels.

The fear of machines turning evil is another red herring. The real worry isn’t malevolence, but competence. A superintelligent AI is by definition very good at attaining its goals, whatever they may be, so we need to ensure that its goals are aligned with ours. Humans don’t generally hate ants, but we’re more intelligent than they are – so if we want to build a hydroelectric dam and there’s an anthill there, too bad for the ants. The beneficial-AI movement wants to avoid placing humanity in the position of those ants.

In fact, the main concern of the beneficial-AI movement isn’t with robots but with intelligence itself: specifically, intelligence whose goals are misaligned with ours. To cause us trouble, such misaligned superhuman intelligence needs no robotic body, merely an internet connection – this may enable outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand. Even if building robots were physically impossible, a super-intelligent and super-wealthy AI could easily pay or manipulate many humans to unwittingly do its bidding.
The robot misconception is related to the myth that machines can’t control humans. Intelligence enables control: humans control tigers not because we are stronger, but because we are smarter.

(Source: https://cturtle.co/)

Go is a game that has been around for 3000 years. It is widely accepted as the most challenging strategy game that exists. It takes years of playing for several hours every day to master the game. In other words, even though it has simple rules, it is not a simple game to excel at. Surprise! Deep Mind managed to create a machine that could master the game, without being programmed with explicit rules and without being taught by a professional Go player. AlphaGo mainly played against itself and learned from this self-play. At its core, it learned like a human learns, by looking at the board, evaluating the options, making moves, and learning from mistakes - it just did it a lot faster than any human can.

This is extremely exciting because, at its core, what it means is that computer scientists have had all the tools they needed to do this for years. Neural networks have been known about and discussed since the middle of the last century. All it really took was simply getting creative with them, applying them in new ways. AlphaGo beating the world’s best Go player proves that AI has the potential to do anything. It can learn anything and understand anything, and from that learning and understanding it can accomplish what humans can accomplish in a much shorter period of time.

You’re probably wondering what this all means. We’re much closer to the dream of an AI best friend than most of us would have dared to imagine a few years ago. AlphaGo can learn the most complex, intuition and creativity based logic game known to man and it didn’t do so through a finite database or search trees alone. It learned from practice and experience, just like we do, and the ability to create amazing new solutions to ancient puzzles suggests a realm of digital creativity never before fathomed. AlphaGo is not like other game playing AIs that have come before it. It is the future of intelligent and intuitive machines, one that we plan to turn toward more than just board games. From practical applications to that friend you’ve been hoping for, AlphaGo is sure to be the first of a new generation of self-learning intuitive AIs that go above and beyond the limited calculating capacities of its older siblings and contemporaries. The AI winter is over.

(Source: https://medium.com)