AI Policies: What is the world doing to make them secure

A couple of months ago the internet went berserk with the news of Facebook pulling the plug out on two bots, which started communicating in their own language. Imaginations and headlines went wild with the possibilities: malicious AI is taking over, the doomsday is here with the bots of the Apocalypse. Although the real story was quite different (the bots were turned off because they were designed to communicate with humans, not with each other, thus they were not delivering the expected results), the outcome was simply panic.What we can learn from this is that humans are afraid of their own creation - the artificial intelligence.

AI can transform gargantuan amounts of complex information into insight. It has the potential to present solutions, reveal secrets and solve problems. But before we get to the good part, we need to take care of development and deployment. In order to be able to use them, AI systems need to have the same ethical principles, moral values, professional codes, and social norms we follow. Some of us are excited about the opportunities AI provides, others are suspicious. To become widespread, AI needs to be designed in a way that allows people to understand it, use it and trust it. To ensure the acceptance of AI, public policies should help society deal with AI’s inevitable failures and facilitate adaptation.

Where are we now?

Policies can help AI’s progress or hamper it. We are witnessing a shift in the bottleneck to using AI products from technology to policymaking. Regulation is slow to respond to the cost of compliance or the adoption and development of innovations. Thorough and well-thought policies can influence the rate and direction of innovation by creating stimulus for the private sector. In order to grasp the current situation, we will take a look at the major players in AI technologies whose decisions will be influencing the future of policy making. Yes, you guessed it: China and USA (Europe also deserves a mention). If a country with AI research expertise wishes to participate as a producer it should be ready for tense labor market competition from the U.S. and China.

USA

On October 12, 2016, President Obama’s Executive Office published two reports that laid out its plans for the future of artificial intelligence. The report entitled “Artificial Intelligence, Automation and the Economy,” concluded that AI-driven automation suggests the need for aggressive public policies and a more robust safety net in order to combat labor disruption. The report elaborates on the topics of the previous one: Preparing for the Future of Artificial Intelligence, which recommended the publishing of a report on the economic impacts of artificial intelligence. The focus of AI capabilities is the automation of tasks which have required manual labor, which will provide new possibilities for the economy. However, the disruption of the current livelihood of some people is inevitable. The report’s objective is to find how to increase the benefits and mitigate the costs.

AI isn’t a science project; it’s commercially important.

The report proposes that three broad strategies are followed to ease the AI automation in the economy: first, invest and develop AI; second, educate and train workers for the future jobs, and, finally, aid workers in the transition and empower them to ensure broadly shared growth. Since AI automation will transform the economy, policymakers need to create or update, strengthen and adapt policies. The primary economic effects under consideration are the beneficial contribution to productivity growth, the new skills that the job market will demand (especially higher-level technical skills); the disbalance the impact of AI will create on wage and education levels, job types and locations; the loss of jobs which might be long term, depending on the policy responses.

China

For the past four years, the US and China have been heavily investing in AI especially compared to other countries. Just till recently, the US seemed like the leader in the tech race, but 2 years ago China has outdone the US in research output. China is emerging as a leader, not a follower. Government is backing research and development and thus driving China’s economy forward. The total value of AI industries will surpass 1 trillion yuan ($147.80 billion).

On July 20, China’s State Council issued the “Next Generation Artificial Intelligence Development Plan” (新一代人工智能发展规划), which articulates an ambitious agenda for China to lead the world in AI. China intends to pursue a “first-mover advantage” to become the “premier global AI innovation center” by 2030.  And Wan Gang, the Minister of Science and Technology, stated that China plans to launch a national AI plan, which will strengthen AI development and application, introduce policies to contain risks associated with AI, and work toward international cooperation. The plan will also provide funds to back these endeavors up.

The guideline states that developing AI is a “complicated and systematic project” and needs a coordinated AI innovation system- not only for the technology, but for the products as well. It goes on stating that AI in China should be used to promote the country’s technology, social welfare, economy, provide national security, and help the world in general.

The guideline advises that trans-boundary research needs to connect AI with subjects like psychology, cognitive science, mathematics, and economics. As far as platform construction goes, open-source computing platforms should promote coordination among different hardware, software and clouds. This will naturally increase the need of more AI professionals and scientists should who need to be prepared for work.

Europe

The International Business Machines Corporation (IBM) is actively engaged in global discussion about making AI ethical and beneficial. It is working not only internally, but with collaborators and competitors as well.

Because of the constant change in development, AI is making it difficult for any regulation agency to keep up with the progress. This is making meaningful and timely guidance almost impossible. On the other hands, issues like data privacy and ownership have been discussed in the EU. An algorithm for transparency and accountability has also been considered.

In 2018, the General Data Protection Regulation will be rolled out in the EU. It will restrict automated individual decision-making (algorithms making decisions on user-level predictors) that affects users. This law provides the “right to explanation:” a user can request an explanation why the algorithm has chosen him/her.

Safety is important, but so are fairness, equality and inclusiveness, which should be included in the AI systems. That’s why we need policies and regulations: to ensure AI is being used to the benefit of all. IBM is working with governments, media, regulatory agencies and industry sectors: everyone, who is willing to have a reasonable discussion on the ethical issues of AI. The aim is to clearly identify the potential and limits of AI and how to make the best use of it.

Who is it up to?

On a shorter term, it is up to the policymakers and lawyers. In the near future, government representatives need to have the technical expertise in AI to justify decisions. More research is needed on the security, privacy and the societal implications of AI used. For example, instead of cross-examining a person, lawyers may need to cross-examine an algorithm.

As with everything technological, there is a definite uncertainty about how strongly these effects will be felt. Maybe AI won’t have a large effect on the economy. But the other option is for the economy to experience a larger shock: changes in the labor market, employees without relevant work skills and in a desperate need of a training. Although no definitive decision could be taken or a deadline for policies setting, continued involvement of the government with the industry, technical and policy experts will play an important role.

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Artificial Intelligence Becoming Human. Is That Good or Bad?

The term “artificial intelligence” has been driving people’s imaginations wild even before 1955 when the term was coined to describe an emerging computer science discipline. Today the term includes a variety of technologies to improve the human life and the list is ever growing. Starting with Alexa and self-driving cars finishing with love robots, your newsfeed is constantly full of AI updates. Your newsfeed is also a product of (somewhat) well-implemented algorithm. The good news? Just like the rest of the AI technologies, your newsfeed is self-learning and constantly changing, trying to improve your experience. The bad news? A lot of people know why but nobody can really explain why the most advanced algorithms work. And that’s where things can go wrong. And that’s where things can go wrong.

The Good AI

The AI market is blooming. The profitable mix of media attention, hype, startups and adoption by enterprises is making sure that AI is a household topic. A Narrative Science survey found that 38% of enterprises are already using AI and Forrester Research predicted that in 2017 the investments in AI will grow by 300% compared with 2016.

But what good can artificial intelligence do today?

Natural language generation

This capability of AI is used to generate reports, summarize business intelligence insights and automate customer service, AI can use this ability to produce text from data.

Speech recognition

Interactive voice response systems and mobile applications rely on AI ability to recognize speech. It transcribes and transforms human speech into form usable by a computer application.

Image recognition

This has been already successfully used to detect problematic persons at airports, for retail, etc.

Virtual agents/chatbots

These virtual agents are used in customer service and support, smart home managers. These chatbot systems and advanced AI can interact with humans. There are machine learning platforms which can design, train and deploy models into applications, processes and other machines, by providing algorithms, APIs, development and training data.

Decision management for enterprise

Engines that use rules and logic into AI systems and are used for initial setup/training and ongoing maintenance and tuning? Check. This technology has been used for a while now for decision management by enterprise applications and assisting automated decision-making. There is also AI-optimized hardware with the power to process graphics and designed to run AI computational jobs.

AI for biometrics

On a more personal level, the use of AI in biometrics enables more natural interactions between humans and machines, relying on image and touch recognition, speech, and body language. By using scripts and other ways to automate human action to support efficient business processes, robots are capable of executing tasks or processes instead of humans.

Fraud detection and security

Natural language processing (NLP) uses and supports text analytics by understanding sentence structure and meaning, sentiment and intent through statistical and machine learning methods. It is currently used in fraud detection and security.

The “Black Box” of AI

At the beginning AI breached out in two directions: machines should reason according to rules and logic (everything is visible in the code); machines should use biology and learn from observing and experiencing (a program generates an algorithm based on example data). Today machines ultimately program themselves based on the latter approach. Since there is no hand-coded system which can be observed and examined, deep learning is particularly a “black box.”

It is crucial to make sure we know when failures in the AI occur because they will. In order to do that, we need to know how techniques like deep learning work. Recognizing abstract things. In simple systems, recognition is based on physical attributes like outlines and colour; on the next level- more complex things like basic shapes, textures, etc. The top level can recognize all the levels and the whole not just as a sum of its parts.

There is the expectation that these techniques will be used to diagnose diseases, make trading decisions and transform whole industries. But it shouldn’t happen before we manage to make deep learning more understandable especially to their creators and accountable for their uses. Otherwise there is no way to predict failures.

Today mathematical models are already being used to find out who is approved for a loan and who gets a job. But deep learning represents a different way to program computers.  “It is a problem that is already relevant, and it’s going to be much more relevant in the future,” says Tommi Jaakkola, a professor at MIT who works on applications of machine learning. “Whether it’s an investment decision, a medical decision, or maybe a military decision, you don’t want to just rely on a ‘black box’ method.”

Starting in the summer of 2018, the European Union will probably require that companies be able to explain decisions made by automated systems. Easy right? Not really: this task might be impossible if the apps and the websites use deep learning. Even if it comes to something simple like recommending products or playing songs. Those services are run by computers which have programmed themselves. Even the engineers who have build them will not be able to fully clarify the way the computers reach the results.

“It might be part of the nature of intelligence that only part of it is exposed to rational explanation. Some of it is just instinctual.”

With the advance of technology, logic and reason might need to step down and leave some room for faith. Just like human reasoning and logic, we can’t always explain why we’ve taken a decision. However, this is the first time we are dealing with machines, which are not understandable by even the people who engineered them. How will this influence our relationship with technology? A hand-coded system is pretty straightforward, but any machine-learning technology is way more convoluted. Yes, not all AI tech will be this difficult to understand, but deep learning is a black box by design.

AI works a bit like the neural network and its center- the brain: you can’t look inside it to find out how it works because a network’s reasoning is embedded in the behaviour of thousands of simulated neurons. These neurons are arranged into dozens or even hundreds of intricately interconnected layers. The first layer receives input and then performs calculations before giving an a new signal as output. The results are fed to neurons in the next layer and so on.

Because there are many layers in a deep network, they are able to recognize things at different levels of abstraction. If you want to build an app, let’s say “Not a HotDog” (“Silicon Valley,” anyone?), you need to know what  a hot dog looks like. A system might be designed to recognize hot dogs based on outlines or color. Higher layers will recognize more complex things like texture and details like condiments.

But just as many aspects of human behavior can’t be explained in detail, it might be the case that we won’t be able to explain everything AI does.  “Even if somebody can give you a reasonable-sounding explanation [for his or her actions], it probably is incomplete, and the same could very well be true for AI,” says Clune, of the University of Wyoming. “It might just be part of the nature of intelligence that only part of it is exposed to rational explanation. Some of it is just instinctual, or subconscious, or inscrutable.”

Just like civilizations have been built on a contract of expected behaviour, we might need to design AI system to respect and fit into our social norms. Whatever robot or a system we created, it is important that their decision-making is consistent with our ethical judgements.

The AI Future

Participants in a recent survey were asked about the most worrying notion about AI. The results were as expected: participants were most worried by the notion of a robot that would cause them physical harm. Naturally, machines with close physical contact like self-driving cars and home managers were viewed as risky. However, when it cоmes to statistics, languages, personal assistants: people are more than willing to use AI in everyday tasks. The many potential social and economic benefits from the technology depend on the environment in which they evolve, says the Royal Society.

A robot animated by AI is known as “embodiment.” Thus applications that involved embodiment were viewed as risky. As data scientist Cathy O’Neil has written, algorithms are dangerous if they posses scale, their working are a secret and their effects are destructive. Alison Powell, an assistant professor at the London School of Economics believes that this mismatch between perceived and potential risk is common with new technologies. “This is part of the overall problem of the communication of technological promise: new technologies are so often positioned as “personal” that perception of systematic risk is impeded.”

Philosophers, computer scientists and techies make the distinction between “soft” and “hard” AI. The main difference? Hard AI’s main goal is to mimic the human mind. As the Wall Street Journal and MIT lecturer Irving Wladawsky-Berger explained, soft AI’s main purpose is to be statistically oriented and use its computational intelligence methods to address complex problems based on the analysis of vast amounts of information using sophisticated algorithms. For most of us soft AI is already an everyday part of our daily routine: from the GPS to ordering food online. According to Wladawsky-Berger, hard AI is “a kind of artificial general intelligence that can successfully match or exceed human intelligence in cognitive tasks such as reasoning, planning, learning, vision and natural language conversations on any subject.”

AI is already used to build devices that cheat and deceive or to outsmart human hackers. It is quickly learning from our behavior and people are building robots who are so humanlike they might be our lovers. AI is also learning right from wrong. Mark Riedl and Brent Harrison from the School of Interactive Computing at the Georgia Institute of Technology are leading a team who is trying to instill human ethics to AIs by using stories. Just like in real life we teach human values to children by reading them stories, AI learns to distinguish wrong from right, bad from good. Just like civilizations have been built on a contract of expected behaviour, we might need to design AI system to respect and fit into our social norms. Whatever robot or a system we created, it is important that their decision-making is consistent with our ethical judgements.

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7 Image Recognition Uses of the Future

Did you know that image recognition is one of the main technologies that skyrockets the development of self-driving cars?

Image identification powered by innovative machine learning has already been embedded in a number of fields with impressive success. It is used for automated image organization of large databases and visual websites, as well as face and photo recognition on social networks such as Facebook. Image recognition makes image classification for stock websites easier, and even fuels marketers’ creativity by enabling them to craft interactive brand campaigns.  

Beyond the common uses of image recognition we have gotten accustomed to, the revolutionizing technology goes far beyond our imagination. Here are seven daring applications of computer vision that might as well belong in a science fiction novel - but are getting very close to reality today.

#1. Creating city guides

Can you imagine choosing your next travel destination on the basis of real-time location information from Instagram photos that other tourists have posted? Well, it’s already out there. Jetpac created its virtual “city guides” back in 2013 by using shared visuals from Instagram.

By employing image recognition, Jetpac caught visual cues in the photos and analyzed them to offer live data to its users. For example, on the basis of images, the app could tell you whether a cafe in Berlin is frequented by hipsters, or it’s a wild country bar. This way, users receive local customized recommendations at-a-glance.  

In August 2014, Jetpac was acquired by Google, joining the company’s Knowledge team. Its knowhow is said to be helping Google’s development of visual search and Google Glass, the ‘ubiquitous computer’ trial of the tech giant.

#2. Powering self-driving cars

In the last years, self-driving cars are the buzz in the auto industry and the tech alike. Autonomous vehicles are already being actively tested on U.S. roads as we speak. Forty-four companies are currently working on different versions of self-driving vehicles. Computer vision is one of the main technologies that makes these advancements possible, and is fueling their rapid development and enhanced safety features.

To enable autonomous driving, artificial intelligence is being taught to recognize various objects on roads. They include pathways, moving objects, vehicles, and people. Image recognition technology can also predict speed, location and behavior of other objects in motion. AI companies such as AImotive are also instructing their software to adapt to different driving styles and conditions. Researchers are close to creating AI for self-driving cars that can even see in the dark.

https://www.youtube.com/watch?v=sIlCR4eG8_o

#3. Boosting augmented reality applications and gaming

Augmented reality experiments have long tantalized people’s imagination. With image recognition, transposition of digital information on top of what we see in the world is no longer a futuristic dream. Unlike virtual reality, augmented reality does not replace our environment with a digital one. It simply adds some great perks to it.

You can see the most common applications of augmented reality in gaming. A number of new games use image recognition to complement their products with an extra flair that makes the gaming experience more immediate and ‘real.’ With neural networks training, developers can also create more realistic game environments and characters.

Image recognition has also been used in powering other augmented reality applications, such as crowd behavior monitoring by CrowdOptic and augmented reality advertising by Blippar.

#4. Organizing one’s visual memory

Here’s for a very practical application of image recognition - making mental notes through visuals. Who wouldn’t like to get this extra skill?

The app Deja Vu, for example, helps users organize their visual memory. When you take a photo, its computer vision technology matches the visual with background information about the objects on it. This means you can instantly get data about books, DVDs, and wine bottles just by taking a photo of their covers or labels. Once in your database, you can search through your photos on the basis of location and keywords.

#5. Teaching machines to see

Besides the impressive number of consumer uses that image recognition has, it is already employed in important manufacturing and industrial processes. Teaching machines to recognize visuals, analyze them, and take decisions on the basis of the visual input holds stunning potential for production across the globe.

Image recognition can make possible the creation of machines that automatically detect defects in manufacturing pipelines. Besides already known faults, the AI-powered systems could also recognize previously unknown defects because of their ability to learn.

There is a myriad of potential uses of teaching machines to perceive our visual world. For example, Xerox scientists are applying deep learning techniques to enable their AI software mimic the attention patterns of the human brain when seeing a photo or a video.

#6. Empowering educators and students

Another inspiring use of image recognition that is already being put in practice is tightly connected with education again - but this time, with improving education of people.

Image recognition is embedded in technologies that enable students with learning disabilities receive the education they need - in a form they can perceive. Apps powered by computer vision offer text-to-speech options, which allow students with impaired vision or dyslexia to ‘read’ the content.

Applications of image recognition in education are not limited to special students’ needs. The technology is used in a range of tools that push the boundaries of traditional teaching. For example, the app Anatomy3D allows discovery of the interconnectedness between organs and muscles in the human body through scanning of a body part. It revolutionizes the way students can explore anatomy and learn about the way our bodies function. Image recognition uses can also help educators find innovative ways to reach ever more distracted students, who are not susceptible to current methods of teaching.   

#7. Improving iris recognition

Iris recognition is a widely used method for biometric identification. It’s most common application is in border security checks, where a person’s identity is verified by scanning their iris. The identification is conducted by analyzing the unique patterns in the colored part of the eye.

Even though iris recognition has been around for a while, in some cases it is not as precise as it’s expected to be. The advancement of image recognition, however, is bringing new possibilities for iris recognition use across industries with improved accuracy and new applications. Most notably, iris identification is already being used in some consumer devices. The smartphones Samsung Galaxy Note7 and Galaxy S8, and Windows Lumia 950 are among the ones already equipped with such a capability.

While recognition is becoming more precise, security concerns over biometrics identification remain, as recently hackers broke the iris recognition of Samsung Galaxy S8. Together with the advancement of computer vision, security measures are also bound to improve to match the new technological opportunities.    

Have you had an experience with AI technology from a movie that years later you seen in real life? Share with the rest of the group and if it enough people like it we can build it together.

The uses of image recognition of the future are practically limitless - they’re only bound by human imagination. What is the practical application of computer vision that you find the most exciting or useful? We’d love to read about it in the comments below.

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