The bipartisan Congressional Artificial Intelligence Caucus is concerned about the lack of coordination between individual federal departments’ AI offices.

Rep. Pete Olson, R-Texas, sitting on a panel next to co-chair Rep. Jerry McNerney, D-Calif., said that he wants the different offices throughout agencies dedicated to developing AI capabilities to interact with each other more to increase efficiency.

“We’re competing against foreign countries who want this technology and want to take the world over,” said Olson, speaking Dec. 5 at the GovernmentCIO AI and RPA in Government conference.

Federal departments across the government have been working on AI independently. The Department of Defense stood up the Joint Artificial Intelligence Center (JAIC) last year, which is working to accelerate AI adoption across the Pentagon. Meanwhile, the Department of Energy also formally announced an AI office. Olson wants to see these types of groups work together to “help us make sure we all stand on one big boat and not several boats,” he said.

“Let’s talk guys, no duplicates, spend our taxpayer dollars wisely,” Olson said. “Don’t double up, triple up efforts. Talk.”

There is at least one example of this type of communication between agencies. The JAIC and General Services Administration recently teamed up on artificial intelligence work. But if Congress is going to take action to facilitate AI development in federal agencies, it needs members who understand the technology. That’s one reason the caucus was formed. Olson said on the panel that the caucus’ mantra is “educate, then legislate.”

“We’re working hard with this caucus to get people informed, educated, so they can go help us legislate,” said Olson, who founded the AI caucus and is retiring after this term.

The two co-chairs of the House of Representatives bipartisan congressional artificial intelligence caucus see significant interest on Capitol Hill for exploring the uses of artificial intelligence, an issue that they repeatedly stressed throughout the panel had a rare bipartisan stamp on it. Now, McNerney said, Congress needs to look at investing more in the technology.

“We’re not spending enough resources right now to do the job,” McNerney said. “We need to increase federal participation in artificial intelligence both in terms of expenditures and in terms of creating job opportunities in the government for artificial intelligence.”

McNerney said Congress needs people who understand risks associated with AI, along with ethical challenges and how those two balance out with rewards. He added that “there’s a lot of nuance” to AI, particularly related to job loss.

“What happens is that any job has aspects that will be susceptible to be replaced by artificial intelligence, but aspects of the job that are not,” McNerney said. “So it’s important to understand what those two are and use the parts that are not susceptible … and weave those into new opportunities that will be presenting themselves.”

While most caucus meetings receive minimal attendance on Capitol Hill, both members said that turnout for the AI caucus has been packed with both members and their staff this year.

“People want to know what this is; they want to know what the future looks like,” McNerney said.

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Today, artificial intelligence is dominating most of the games — from board games to interactive fiction games. They are providing complex, decision-making environments for AI to experiment with. The ability of games to provide interesting and complex problems, offering creativity and expression, has made them one of the most popular and meaningful domain for AI researchers.

Games offer one of the most meaningful domains that can process, interpret and stimulate human behaviour. The current gaming industry is not only deploying better graphics but is also exploring the area of virtual gameplay. The two-way relationship of gaming and AI has begun to tread a new road and it can be said that the gaming industry is largely revolutionising the way AI works.

AI In Gaming Industry

Application of AI to the gaming industry can be dated back to 1956 by Arthur Samuel’s checkers program. Since its first application which could beat professional players to the present day’s AlphaGo, AI in gaming has come a long way.

Today we see an enormous upsurge of AI in game. First Encounter Assault Recon, popularly known as F.E.A.R. and The Last Of Us are some of the most popular games that give a very realistic experience with the use of AI.

How Does Gaming Aid AI?

Games are difficult because of the complexity and the skill that demands of them to play. This complexity of games makes it very desirable for AI to work on. A typical game has about 101685 possible states, whereas the number of protons in the observable universe are just of the order of 1080. This can tell about the degree to which the gaming industry is complicated and rich with data. And where there is plenty of data, AI is always a privilege. With larger sets of training data, AI would have the ability to be less predictable and more spontaneous, thereby making the gameinfinitely interesting and impulsive.

Interaction:

As every game involves players, the interaction of the player with the game is advantageous to AI, as it gives access to the algorithm to study the player experience an emotional behaviour. The study of this game and human interaction proves a key to not only study the human behaviour, but it also makes a way for AI to build a better human-computer interaction system. It further pushes the AI boundaries to study and understand the human-computer interaction systems and address the challenges faced by its applications in the real world.

Decision-Making:

This is the main crux of AI. AI must be able to make decisions by looking at the opponent’s action. There are various models used for decision-making in the game. Markov model is the most popular model. Fine State Machine (FSM) is one of the many AI methods used for decision-making.

Prediction Ability:

Prediction involves anticipating the next move of the player, so that decision-making can be done based on it. This is done using methods like past-pattern recognition and random guess. Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Decision Tree Learning are the algorithms used for prediction. Regression algorithms are used for predicting player behaviour. This process includes situations like predicting times when the player is expected to be in the particular level of the game, what item will the player pick next, when will he move to the other lane, are made. Experimenting with this is virtual games, we implement these algorithms and models in the real world as well.

Intelligence:

Social intelligence and human-computer interaction are the most supreme objectives of AI. These two things are taken into consideration by games and that way they help in AI development. Virtual characters exhibiting human behaviour as well as intelligence.

AI had learnt about the intelligence of computers the most from games, than from any other application, because they provide a virtual platform to test every kind of algorithm. Moreover, they also provide complicated mathematical problems to deal with, so the AI learning is not just restricted to the gaming world.

The success of deep Q-learning in learning to play arcade games with a human-level performance by just looking at and processing the pixels on the screen, is an example of intelligence. The study of intelligence within games not only lets us know more about human intelligence but also about AI intelligence.

The recent Dota2 tournament, ‘The International’, had bots competing with professional players. Although they couldn’t win the match, it must be noted that the ability that AI can be bestowed with, to play games as complicated as Dota2, is remarkable. Another example into the future of AI in games is at the Michigan State University, where a group of researchers have deployed AI to learn a game by learning from every player’s behaviour. It will adapt to individual player’s behaviour and play the next move.

Games offer both entertainment and interaction, in turn having a very high realisation of the affective loop which is very important in gaming. They provide a multitude of fancy features at once — visual art, sound design, graphic design, beautification, are narrative, virtual cinematography, all in one single software. Games are perfect testbeds for AI because they act as the best application of computer creativity. As a result, with the use of computational creativity in the gaming industry, provides a way to advance AI. It not only challenges computer creativity but also advances it.

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New Delhi: French multinational oil and gas company Total announced it has signed an agreement with Google Cloud to jointly develop Artificial Intelligence (AI) solutions for subsurface data analysis for oil and gas exploration and production.

“Total is convinced that applying artificial intelligence in the oil and gas industry is a promising avenue to be explored for optimizing our performance, particularly in subsurface data interpretation. We are excited to work with Google Cloud towards this goal. This builds on the strategy being developed at Total, where A.I. is already used, for example, in predictive maintenance at facilities,” said Marie-Noëlle Semeria, Senior Vice President, Group CTO at Total.

The agreement will focus on the development of AI programs which will make it possible to interpret subsurface images, notably from seismic studies (using Computer Vision technology) and automate the analysis of technical documents (using Natural Language Processing technology), the company said.

It added the programs will allow Total’s geologists, geophysicists, reservoir and geo-information engineers to explore and assess oil & gas fields faster and more effectively.

“We are thrilled to welcome Total in our Advanced Solutions Labs for the development of A.I. solutions. We are keen to engage our best A.I. engineers to work with Total’s geosciences’ experts,” said Paul-Henri Ferrand, President of Global Customer Operations Google Cloud.

The partnership will involve Total geoscientists working together with Google Cloud’s machine learning experts within the same project team based in Google Cloud’s Advanced Solutions Lab in California.

Total started applying artificial intelligence to characterize oil & gas fields using machine learning algorithms in the 1990s.

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2017 was a formative year for artificial intelligence – not just in terms of the advancement of the technology itself, but also for the evolution of our understanding of AI’s impact on our society. Here are my predictions on the two topics that will become the primary focuses of AI in 2018.

Algorithms will change the way we work

When we talk about AI, often we focus on the belief that it is going to take away certain types of jobs (like cashiers or janitorial services). However, we often ignore the fact that this technology will also slowly permeate into most of our lives during work.

I refer to this as the distinction between AI and IA — artificial intelligence vs. intelligence augmentation. Despite the strides we’ve made in AI development and will continue to make in 2018, we are still years away from AI fully replacing human jobs. However, we are much closer to seeing the impact of AI permeate into almost every job and augment human intelligence.

Think about how you spend your days at work. There are likely parts of your day that require repetitive tasks or pattern recognition that could be delegated to someone without as much context and creativity. Take a doctor for example — AI will soon be able to detect and diagnose common diseases more quickly and accurately than humans. Imagine a world where computers read in a patient’s medical history, test results, and scans, and they are able to present doctors with various possible diagnoses and the likelihood that each of them is correct. Now, doctors can spend more of their time interpreting the possible diagnoses, communicating with patients, and developing unique, sustainable treatment plans that are most effective for each specific patient.

A world where doctors are replaced by AI — if that ever happens — is far off. However, a world where doctors are more efficient and effective with the help of AI is coming very soon. In cases like these, AI isn’t replacing anyone’s job; it’s simply allowing people to do their job better.

AI will be held against more scrutiny

AI is incredibly powerful and its adoption will only accelerate as it begins to augment our work and allow us to focus on the parts of our job that are most important. But part of why AI is so attractive is also why it’s so dangerous.

Algorithms are desirable for their ability to make well-informed decisions quickly and accurately. But that same power allows algorithms that are making incorrect decisions (and perpetuating biases) to do so with greater velocity and widespread impact than humans have ever had. As I mentioned in my earlier post about the Dark Side of Artificial Intelligence, there is potential for homogenous data sets to produce biased algorithms that are making important decisions about people’s lives — and we have already started to see the impact of these today (see Microsoft’s Twitter bot “Tay” and Google’s image results for Gorilla).

This is why, as AI becomes more prolific in 2018, it will also become more closely audited and scrutinized. Unfortunately, we will probably endure a few more experiences detailing what happens when AI is left unchecked. However, 2018 will be the year that companies purchasing AI products don’t just ask about the predictive power of an algorithm — they will stipulate that algorithms are tested in advance, interrogating and minimizing their potential unexpected impact.

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There is an old saying that speaks to the current state of AI: “To someone holding a hammer, everything looks like a nail.” As companies, governments, and organizations scramble to be in the vanguard of this new generation of artificial intelligence, they are trying their best to persuade everyone that all of our human shortcomings will be absolved by this technological evolution. But what exactly will it solve? Machine learning is an incredibly powerful tool, but, like any other tool, it requires a clear understanding of the problems to be solved in the first place — especially when those problems involve real humans.

Human versus machine intelligence

There is an oft-cited bit from Douglas Adams’ The Hitchhiker’s Guide to the Galaxy series in which an omniscient computer is asked for the ultimate answer to life and the universe. After 7.5 million years, it provides its answer: the number 42. The computer explains to the discombobulated beings who built it that the answer appears meaningless only because they never understood the question they wanted answered.

What is important is identifying the questions machine learning is well-tailored to answer, the questions it struggles with, and perhaps most importantly, how the paradigmatic shift in AI frameworks is impacting the relationship between humans, their data, and the world it describes. Using neural nets has allowed machines to become uncannily accurate at distinguishing idiosyncrasies in massive datasets — but at the cost of truly understanding what they know.

In his Pulitzer Prize-winning book, Gödel, Escher, Bach: an Eternal Golden Braid, Douglas Hofstadter explores the themes of intelligence. He contemplates the idea that intelligence is built upon tangled layers of “strange loops,” a Möbius strip of hierarchical, abstracted levels that paradoxically wind up where they started out. He believes that intelligence is an emergent property built on self-referential layers of logic and abstractions.

This is the wonder that neural nets have achieved — a multi-layered mesh of nodes and weights that pass information from one tier to the next in a digital reflection of the human brain. However, there is one important rule of thumb in artificial intelligence: The more difficult it is for a human to interpret and process something, the easier it is for a machine, and vice versa.

Calculating digits of π, encrypting messages using unimaginably huge prime numbers, and remembering a bottomless Tartarean abyss of information can occur within the blink of an eye using a computer, which manages to outperform millennia of human calculations. And yet humans can recognize their friend’s face in an embarrassing baby photo, identify painters based on brush strokes, and make sense of overly verbose and ruminating blog entries. These are domains that machine learning has made vast improvements in, but it is no wonder that as the human brain-inspired architecture of neural nets brings machines up to parity, and in some cases beyond, in areas of human cognition, machines are beginning to suffer some of the same problems humans do.

Nature or nurture?

By design, we are unable to know what neural nets have learned, and instead we often keep feeding the system more data until we like what we see. Worse yet, the knowledge it has “learned” is not discrete principles and theories, but rather contained in a vast network that is incomprehensible to humans. While Hofstadter might have contemplated artificial intelligence as a reflection of human intelligence, modern AI architects have no tendency to share the same preoccupation. Consequently, modern neural nets, while highly accurate, do not elucidate any understanding of the world for us. In fact, there are several well-publicized instances where AI went afoul, manifesting in a socially unacceptable reality. Within a day of Microsoft’s AI chatbot Tay being active, it learned from Twitter users how to craft misogynistic, racist, and transphobic tweets. Did Tay learn a conceptual sociohistorical theory of gender or race? I would argue not.

Why AI can’t be left unattended

Paradoxically, even if we assume that the purpose of an AI isn’t to understand human concepts at all, these concepts often materialize anyway. As another example of misguided AI, an algorithm was used to predict the likelihood of someone committing future crimes. Statistically based software models learned racial biases, assigning higher risks to black defendants with virtually no criminal records, if any, than to white defendants with extensive histories of violent crime. Facial recognition software is also known to have its biases, to the point that a Nikon camera was unable to determine if a Taiwanese-American woman had her eyes open or not. Machine learning is only as good as the data it is built upon, and when that data is subject to human biases, AI systems inherit these biases. Machines are effective at learning from data, but unlike humans, have little to no proficiency when it comes to taking into account all the things they don’t know, the things missing from the data. This is why even Facebook, which is able to devote massive AI resources to its efforts to eliminate terroristic posts, concedes that the cleanup process ultimately depends on human moderators. We should be rightfully anxious about firing up an AI, whose knowledge is unknowable to us, and leaving it to simmer unattended.

The AI community cannot be haphazard about throwing open the AI gates. Machine learning works best when the stakeholders’ problems and goals are clearly identified, allowing us to chart an appropriate course of action. Treating everything as a nail is likely to waste resources, erode users’ trust, and ultimately lead to ethical dilemmas in AI development.

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Dive Brief:

• Artificial intelligence can only reach its full potential with the help of human traits, says Demis Hassabis, founder of DeepMind, and coauthors in a paper published Friday in Neuron, according to Technology Review.
• The authors say improving AI will require better understanding of how the brain works.
• AI startup Deep Mind was purchased by Google for $650 million in 2014.

Dive Insight:

Ensuring the human side of the equation is included in AI is a problem several companies are looking to address. If programmers involved in AI development look too much at the tech and not enough at the human side, they could come up short in delivering on the promises of AI.

Earlier this month, Google announced a research initiative to focus on the “human side” of AI and how to make it broadly inclusive.

PwC recently predicated Global GDP will be 14% higher in 2030 because of artificial intelligence. By those projections, AI will inject $15.7 trillion into the global economy. But to get there AI systems must be trained well. There have already been a few examples of bias of various kinds built into the foundation of an AI program that can then carry through to the applications where that AI is used. For example, research released in April showed that AI programs can exhibit racial and gender biases, The Guardian reports.

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Microsoft Corp. is pushing further into the artificial intelligence market, announcing a new lab dubbed Microsoft Research AI, which is aimed at developing more general-purpose learning systems.

The lab, which will be located at its Redmond, Wash., headquarters will house more than 100 scientists from all areas of AI: learning, natural language, perception and reasoning. The idea behind the lab is multifaceted, with one goal being the creation of AI-enabled systems that can handle a variety of problems or tasks rather than a single product or device to tackle a particular problem, like getting around in traffic.

Microsoft said the integrated approach lets it develop tools that can do complex, multifaceted tasks. “Every day, computers are getting better at doing individual tasks like recognizing faces in photos or words in a conversation, using functionality such as pattern recognition and classification,” said Harry Shum, executive vice president of Microsoft AI and Research Group, in a blog post. “We believe AI will be even more helpful when we can create tools that combine those functions and add some of the abilities that come naturally to people.”

Ethics and Intelligence

Shum said the army of scientists and engineers will work closely with Microsoft’s research labs and product groups. They will be tasked with solving some of the toughest problems in AI and to accelerate the integration of advances into products and services. Another goal of the research lab is to make sure AI development remains transparent and ethical.

“As technology that uses AI gets smarter, we want to ensure that we take a responsible approach to our progress—and one that will ultimately provide the most benefit to our customers and to society as a whole,” said Shum. “This is uncharted territory, and we recognize that the decisions we make will have profound implications.”

Microsoft’s new lab puts it it in direct competition with other research firms focused on AI. With most of them racing to develop general learning systems, the company will be going up against DeepMind in the U.K. and Google Brain in San Francisco, both of which are units of Alphabet Inc. OpenAI is another AI research lab that’s backed by Elon Musk and Peter Thiel, reported Bloomberg. For the software giant, AI is becoming an important aspect of its business, showing up in Cortana, its voice-activated virtual assistant and in its cloud computing offering. It also overhauled the structure of the company last week to better align with the cloud business and advanced technologies.

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