Daily Archives: February 15, 2022

When TikTok videos emerged in 2021 that seemed to show Tom Cruise making a coin disappear and enjoying a lollipop, the account name was the only obvious clue that this wasnt the real deal. The creator of the deeptomcruise account on the social media platform was using deepfake technology to show a machine-generated version of the famous actor performing magic tricks and having a solo dance-off.

One tell for a deepfake used to be the uncanny valley effect, an unsettling feeling triggered by the hollow look in a synthetic persons eyes. But increasingly convincing images are pulling viewers out of the valley and into the world of deception promulgated by deepfakes.

The startling realism has implications for malevolent uses of the technology: its potential weaponization in disinformation campaigns for political or other gain, the creation of false porn for blackmail, and any number of intricate manipulations for novel forms of abuse and fraud. Developing countermeasures to identify deepfakes has turned into an arms race between security sleuths on one side and cybercriminals and cyberwarfare operatives on the other.

A new study published in the Proceedings of the National Academy of Sciences USA provides a measure of how far the technology has progressed. The results suggest that real humans can easily fall for machine-generated facesand even interpret them as more trustworthy than the genuine article. We found that not only are synthetic faces highly realistic, they are deemed more trustworthy than real faces, says study co-author Hany Farid, a professor at the University of California, Berkeley. The result raises concerns that these faces could be highly effective when used for nefarious purposes.

We have indeed entered the world of dangerous deepfakes, says Piotr Didyk, an associate professor at the University of Italian Switzerland in Lugano, who was not involved in the paper. The tools used to generate the studys still images are already generally accessible. And although creating equally sophisticated video is more challenging, tools for it will probably soon be within general reach, Didyk contends.

The synthetic faces for this study were developed in back-and-forth interactions between two neural networks, examples of a type known as generative adversarial networks. One of the networks, called a generator, produced an evolving series of synthetic faces like a student working progressively through rough drafts. The other network, known as a discriminator, trained on real images and then graded the generated output by comparing it with data on actual faces.

The generator began the exercise with random pixels. With feedback from the discriminator, it gradually produced increasingly realistic humanlike faces. Ultimately, the discriminator was unable to distinguish a real face from a fake one.

The networks trained on an array of real images representing Black, East Asian, South Asian and white faces of both men and women, in contrast with the more common use of white mens faces in earlier research.

After compiling 400 real faces matched to 400 synthetic versions, the researchers asked 315 people to distinguish real from fake among a selection of 128 of the images. Another group of 219 participants got some training and feedback about how to spot fakes as they tried to distinguish the faces. Finally, a third group of 223 participants each rated a selection of 128 of the images for trustworthiness on a scale of one (very untrustworthy) to seven (very trustworthy).

The first group did not do better than a coin toss at telling real faces from fake ones, with an average accuracy of 48.2 percent. The second group failed to show dramatic improvement, receiving only about 59 percent, even with feedback about those participants choices. The group rating trustworthiness gave the synthetic faces a slightly higher average rating of 4.82, compared with 4.48 for real people.

The researchers were not expecting these results. We initially thought that the synthetic faces would be less trustworthy than the real faces, says study co-author Sophie Nightingale.

The uncanny valley idea is not completely retired. Study participants did overwhelmingly identify some of the fakes as fake. Were not saying that every single image generated is indistinguishable from a real face, but a significant number of them are, Nightingale says.

The finding adds to concerns about the accessibility of technology that makes it possible for just about anyone to create deceptive still images. Anyone can create synthetic content without specialized knowledge of Photoshop or CGI, Nightingale says. Another concern is that such findings will create the impression that deepfakes will become completely undetectable, says Wael Abd-Almageed, founding director of the Visual Intelligence and Multimedia Analytics Laboratory at the University of Southern California, who was not involved in the study. He worries scientists might give up on trying to develop countermeasures to deepfakes, although he views keeping their detection on pace with their increasing realism as simply yet another forensics problem.

The conversation thats not happening enough in this research community is how to start proactively to improve these detection tools, says Sam Gregory, director of programs strategy and innovation at WITNESS, a human rights organization that in part focuses on ways to distinguish deepfakes. Making tools for detection is important because people tend to overestimate their ability to spot fakes, he says, and the public always has to understand when theyre being used maliciously.

Gregory, who was not involved in the study, points out that its authors directly address these issues. They highlight three possible solutions, including creating durable watermarks for these generated images, like embedding fingerprints so you can see that it came from a generative process, he says.

The authors of the study end with a stark conclusion after emphasizing that deceptive uses of deepfakes will continue to pose a threat: We, therefore, encourage those developing these technologies to consider whether the associated risks are greater than their benefits, they write. If so, then we discourage the development of technology simply because it is possible.

Excerpt from:

Humans Find AI-Generated Faces More Trustworthy Than the Real Thing - Scientific American

On Feb. 9, the World Health Organization (WHO) announced a new policy brief entitled, Ageism in artificial intelligence for health. The brief discusses legal, non-legal, and technical measures that can be used to minimize the risk of worsening or creating ageism through artificial intelligence (AI) technologies.

The press release on the policy brief states that AI technologies are revolutionizing many fields including public health and medicine for older people where they can help predict health risks and events, enable drug development, support the personalization of care management, and much more.

The release adds that there are concerns that AI, if left unmonitored, could further ageism and challenge the quality of healthcare that older individuals receive. Older individuals are often underrepresented in AI data and there are flawed assumptions surrounding how older people live or interact with technology.

That said, The following eight considerations could ensure that AI technologies for health address ageism and that older people are fully involved in the processes, systems, technologies and services that affect them.

Alana Officer, unit head, demographic change and healthy ageing for WHO was quoted in the release saying that The implicit and explicit biases of society, including around age, are often replicated in AI technologies. To ensure that AI technologies play a beneficial role, ageism must be identified and eliminated from their design, development, use and evaluation. This new policy brief shows how.

The release concludes that the policy brief aligns with the Global report on ageism that was produced by WHO in collaboration with Office of the United Nations High Commissioner for Human Rights, United Nations Department of Economic and Social Affairs, and United Nations Population Fund. The report launched in March 2021.

More here:

WHO Releases Policy Brief on AI and Ageism - Healthcare Innovation

Last weeks announcement of AlphaCode, DeepMinds source codegenerating deep learning system, created a lot of excitementsome of it unwarrantedsurrounding advances in artificial intelligence.

As Ive mentioned in my deep dive on AlphaCode, DeepMinds researchers have done a great job in bringing together the right technology and practices to create a machine learning model that can find solutions to very complex problems.

However, the sometimes-bloated coverage of AlphaCode by the media highlights the endemic problems with framing the growing capabilities of artificial intelligence in the context of competitions meant for humans.

For decades, AI researchers and scientists have been searching for tests that can measure progress toward artificial general intelligence. And having envisioned AI in the image of the human mind, they have turned to benchmarks for human intelligence.

Being multidimensional and subjective, human intelligence can be difficult to measure. But in general, there are some tests and competitions that most people agree are indicative of good cognitive abilities.

Think of every competition as a function that maps a problem to a solution. Youre provided with a problem, whether its a chessboard, a go board, a programming challenge, or a science question. You must map it to a solution. The size of the solution space depends on the problem. For example, go has a much larger solution space than chess because it has a larger board and a bigger number of possible moves. On the other hand, programming challenges have an even vaster solution space: There are hundreds of possible instructions that can be combined in nearly endless ways.

But in each case, a problem is matched with a solution and the solution can be weighed against an expected outcome, whether its winning or losing a game, answering the right question, maximizing a reward, or passing the test cases of the programming challenge.

When it comes to us humans, these competitions really test the limits of our intelligence. Given the computational limits of the brain, we cant brute-force our way through the solution space. No chess or go player can evaluate millions or thousands of moves at each turn in a reasonable amount of time. Likewise, a programmer cant randomly check every possible set of instructions until one results in the solution to the problem.

We start with a reasonable intuition (abduction), match the problem to previously seen patterns (induction), and apply a set of known rules (deduction) continuously until we refine our solution to an acceptable solution. We hone these skills through training and practice, and we become better at finding good solutions to the competitions.

In the process of mastering these competitions, we develop many general cognitive skills that can be applied to other problems, such as planning, strategizing, design patterns, theory of mind, synthesis, decomposition, and critical and abstract thinking. These skills come in handy in other real-world settings, such as business, education, scientific research, product design, and the military.

In more specialized fields, such as math or programming, tests take on more practical implications. For example, in coding competitions, the programmer must decompose a problem statement into smaller parts, then design an algorithm that solves each part and put it all back together. The problems often have interesting twists that require the participant to think in novel ways instead of using the first solution that comes to mind.

Interestingly, a lot of the challenges youll see in these competitions have very little to do with the types of code programmers write daily, such as pulling data from a database, calling an API, or setting up a web server.

But you can expect a person who ranks high in coding competitions to have many general skills that require years of study and practice. This is why many companies use coding challenges as an important tool to evaluate potential hires. Otherwise said, competitive coding is a good proxy for the effort that goes into making a good programmer.

When competitions, games, and tests are applied to artificial intelligence, the computational limits of the brain no longer apply. And this creates the opportunity for shortcuts that the human mind cant achieve.

Take chess and go, two board games that have received much attention from the AI community in the past decades. Chess was once called the drosophila of artificial intelligence. In 1996, DeepBlue defeated chess grandmaster Garry Kasparov. But DeepBlue did not have the general cognitive skills of its human opponent. Instead, it used the sheer computational power of IBMs supercomputers to evaluate millions of moves every second and choose the best one, a feat that is beyond the capacity of the human brain.

At the time, scientists and futurists thought that the Chinese board game go would remain beyond the reach of AI systems for a good while because it had a much larger solution space and required computational power that would not become available for several decades. They were proven wrong in 2016 when AlphaGo defeated go grandmaster Lee Sedol.

But again, AlphaGo didnt play the game like its human opponent. It took advantage of advances in machine learning and computation hardware. It had been trained on a large dataset of previously played gamesa lot more than any human can play in their entire life. It used deep reinforcement learning and Monte Carlo Tree Search (MCTS)and again the computational power of Googles serversto find optimal moves at each turn. It didnt do a brute-force survey of every possible move like DeepBlue, but it still evaluated millions of moves at every turn.

AlphaCode is an even more impressive feat. It uses transformersa type of deep learning architecture that is especially good at processing sequential datato map a natural language problem statement to thousands of possible solutions. It then uses filtering and clustering to choose the 10 most-promising solutions proposed by the model. Impressive as it is, however, AlphaCodes solution-development process is very different from that of a human programmer.

When thought of as the equivalent of human intelligence, advances in AI lead us to all kinds of wrong conclusions, such as robots taking over the world, deep neural networks becoming conscious, and AlphaCode being as good as an average human programmer.

But when viewed in the framework of searching solution spaces, they take on a different meaning. In each of the cases described above, even if the AI system produces outcomes that are similar to or better than those of humans, the process they use is very different from human thinking. In fact, these achievements prove that when you reduce a competition to a well-defined search problem, then with the right algorithm, rules, data, and computation power, you can create an AI system that can find the right solution without going through any of the intermediary skills that humans acquire when they master the craft.

Some might dismiss this difference as long as the outcome is acceptable. But when it comes to solving real-world problems, those intermediary skills that are taken for granted and not measured in the tests are often more important than the test scores themselves.

What does this mean for the future of human intelligence? I like to think of AIat least in its current formas an extension instead of a replacement for human intelligence. Technologies such as AlphaCode cannot think about and design their own problemsone of the key elements of human creativity and innovationbut they are very good problem solvers. They create unique opportunities for very productive cooperation between humans and AI. Humans define the problems, set the rewards or expected outcomes, and the AI helps by finding potential solutions at superhuman speed.

There are several interesting examples of this symbiosis, including a recent project in which Googles researchers formulated a chip floor-planing task as a game and had a reinforcement learning model evaluate numerous potential solutions until it found an optimal arrangement. Another popular trend is the emergence of tools like AutoML, which automate aspects of developing machine learning models by searching for optimal configurations of architecture and hyperparameter values. AutoML is making it possible for people with little experience in data science and machine learning to develop ML models and apply them to their applications. Likewise, a tool like AlphaCode will provide programmers to think more deeply about specific problems, formulate them into well-defined statements and expected results, and have the AI system generate novel solutions that might suggest new directions for application development.

Whether these incremental advances in deep learning will eventually lead to AGI remains to be seen. But whats for sure is that the maturation of these technologies will gradually create a shift in task assignment, where humans become problem finders and AIs become problem solvers.

See the rest here:

Humans and AI: Problem finders and problem solvers - TechTalks

Its no secret that adopting artificial intelligence can be difficult, which discourages some companies from even attempting to use AI in their businesses. However, there might now be a new option for using AI: AI as a service.

AI is a computer programming methodology that allows the computer to make decisions for itself. This can be done in a number of ways, but the most common approach is machine learning. In machine learning, a computer is given data and can eventually learn how to make decisions based on that data.

Machines that learn is an enormous development. Google CEO Sundar Pichai believes artificial intelligence could have more profound implications for humanity than electricity or fire.

When done well, with strategy and execution, customized AI solutions can differentiate a company and create a competitive advantage.

However, for all its potential, most companies struggle to adopt AI. Failure rates for AI projects are high at 80 per cent or more. There are a few reasons for this. First, data is often siloed within companies, making it difficult for AI systems to learn from it.

Second, AI requires specialized talent that is hard to find, and which has to be managed in a different way than most IT personnel. IT teams are used to managing engineers working towards specific goals, such as releasing a feature. By contrast, data scientists are expected to ask and answer questions, such as why something happens. The answers are not known in advance, and the goals are often moving targets.

Third, even when done well, it can take years to realize the benefits. Given this, we can understand why companies sometimes hesitate to initiate these types of projects.

There are other options available, promising to deliver you what your company needs cheaper, faster and better that you ever could on your own.This is known as AI as a Service (AIaaS) for niche problems related to: sales, marketing, human resources, finance and IT.

AIaaS is the delivery of artificial intelligence services through the cloud. This means that companies can access AI services without having to increase their headcount, or add to their overhead. There is no need to invest in the training of your own developers. You dont need to be responsible for owning all the components of your IT stack.

There are many different AI as a service providers, each with their own offerings. In general, these are the main benefits of working with an AIaaS vendor:

Before rushing out to sign up for one of these services, it is important to do your research before selecting a provider, because not all of them are created equal. More generally, there are some other risks you should consider:

Companies face a trade-off when it comes to investments in AI.On the one hand, with custom solutions comes the possibility of differentiation, but the necessary commitment and the risk of failure are high. On the other hand, it is possible to reduce some of those risks with AIaaS, but the payoff may not be sufficiently high. Whether AIaaS is a good gateway into the world of AI depends on the particular problems that companies are looking to address, and the level of risk they can tolerate.

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Adopting AI can be difficult, but there is a new option for CTOs - IT World Canada