Advanced cybersecurity capabilities are essential to safeguard software, systems, and data in a new era of cloud, the internet of things, and other smart technologies. In the real estate industry, for example, companies are concerned about the potential for hijacked elevators, as well as compromised building management and heating and cooling systems. According to Greg Belanger, vice president of security technologies at CBRE, the world's largest commercial real estate services and investment company, securing the enterprise has grown more complex--security teams must be familiar with controls and hardware on new devices, as well as what version of firmware is installed and what vulnerabilities are present. For example, if a heating, ventilation, and air-conditioning (HVAC) system is connected to the internet, he questions, "Is the firmware that's running the HVAC system vulnerable to attack? Could you find a way to traverse that network and come in and attack employees of that company?" Understanding enterprise vulnerabilities are crucial to safeguard physical assets but investing in the right tools can also be a challenge, says Belanger. "Artificial intelligence and machine learning need large sets of data to be effective in delivering the insights," he explains. In the era of cloud-first and industrial internet of things, the perimeter is becoming far more fluid. By applying AI and machine learning to data sets, he says, "You start to see patterns of risk and risky behavior start to emerge." Another priority when securing physical assets is to translate insights into metrics that C-suite leaders can understand, to help boost decision-making. CEOs and members of boards of directors, who are becoming more security savvy, can benefit from aggregated scores for attack surface management. "Everybody wants to know, especially after an attack like Colonial Pipeline, could that happen to us? How secure are we?" says Belanger.

Congress wants to pour hundreds of billions (yes with a B) of dollars into the federal government to increase the nation's competitiveness in emerging technology and, in particular, to accelerate the development of artificial intelligence (AI) technologies that are vital to protecting our national security. The bipartisan support shown for the U.S. Innovation and Competition Act (USICA) – the bill that provides these funds – is a noteworthy and important step in ensuring the United States is resilient and competitive in the 21st century. And that kind of money is nothing to sneeze at. But can the federal government manage to spend it? Thanks to China's aggressive, whole-of-nation approach to emerging technology and the ubiquity of AI technologies that adversaries big and small are now poised to exploit, there is a sudden urgency around AI and national security.

If you sent me a message on Twitter, email or pigeon post, please give me a few days to dig out of the pile that awaits me. You might recall that I mentioned I was off to do some backpacking and climbing in Grand Teton National Park and then eventually would make it to Yellowstone National Park. Yes, the crowds were real, especially for those who stuck to the traditional schedule of sightseeing between 9 a.m. and 5 p.m. I took the early morning and late evening approach and never encountered the infamous parking lot traffic jams. It's that tactic that allowed me to take a ride in an empty T.E.D.D.Y., the autonomous vehicle that is being piloted in Yellowstone this summer.

Cyberattacks are on the rise. AI is part of the threat but also part of the solution. Especially, some of the newer AI strategies (such as adversarial attacks) could be a significant new attack vector. For example, using deepfakes, you could create highly realistic videos, audio or photos and overcome biometric security systems or infiltrate social networks. We will cover adversarial attacks in more detail in the following post.

In an era of AI adoption in industry, stark contrasts in our thinking begin to show about how we leverage computing, data, and inference. This article considers graph technologies in the context of business: enhancing human thinking and enabling data exploration, especially among teams of domain experts augmented by AI applications. Specifically, let's develop and deconstruct the notion of graph thinking. Suppose you have an errand to run, such as shopping for groceries: "Remember to buy eggs and more rice on the way home from work today." The needs are clear, and your approach is well understood. People use phrases such as "It's not rocket science" to describe the level of competency required here. Or perhaps still count on your fingers? In any case, let's call this a "Simple" context.

The cybersecurity industry is rapidly embracing the notion of "zero trust", where architectures, policies, and processes are guided by the principle that no one and nothing should be trusted. However, in the same breath, the cybersecurity industry is incorporating a growing number of AI-driven security solutions that rely on some type of trusted "ground truth" as reference point. This is not a hypothetical discussion. Organizations are introducing AI models into their security practices that impact almost every aspect of their business, and one of the most urgent questions remains whether regulators, compliance officers, security professionals, and employees will be able to trust these security models at all. Because AI models are sophisticated, obscure, automated, and oftentimes evolving, it is difficult to establish trust in an AI-dominant environment. Yet without trust and accountability, some of these models might be considered risk-prohibitive and so could eventually be under-utilized, marginalized, or banned altogether.

Emerging technologies meet both advocates and resistance as users weigh the potential benefits with the potential risks. To successfully implement new technologies, we must start small, in a few simplified forms, fitting a small number of use cases to establish proof of concept before scaling usage. Artificial intelligence is no exception, but with the added challenge of intruding into the cognitive sphere, which has always been the prerogative of humans. Only a small circle of specialists understand how this technology works -- therefore, more education to the broader public is needed as AI becomes more and more integrated into society. I recently connected with Josh Feast, CEO and cofounder of Boston-based AI company Cogito, to discuss the role of AI in the new era of work.

State-of-the-art artificial neural networks (ANNs) require labelled data or feedback between layers, are often biologically implausible, and are vulnerable to adversarial attacks that humans are not susceptible to. On the other hand, Hebbian learning in winner-take-all (WTA) networks, is unsupervised, feed-forward, and biologically plausible. However, an objective optimization theory for WTA networks has been missing, except under very limiting assumptions. Here we derive formally such a theory, based on biologically plausible but generic ANN elements. Through Hebbian learning, network parameters maintain a Bayesian generative model of the data. There is no supervisory loss function, but the network does minimize cross-entropy between its activations and the input distribution. The key is a "soft" WTA where there is no absolute "hard" winner neuron, and a specific type of Hebbian-like plasticity of weights and biases. We confirm our theory in practice, where, in handwritten digit (MNIST) recognition, our Hebbian algorithm, SoftHebb, minimizes cross-entropy without having access to it, and outperforms the more frequently used, hard-WTA-based method. Strikingly, it even outperforms supervised end-to-end backpropagation, under certain conditions. Specifically, in a two-layered network, SoftHebb outperforms backpropagation when the training dataset is only presented once, when the testing data is noisy, and under gradient-based adversarial attacks. Adversarial attacks that confuse SoftHebb are also confusing to the human eye. Finally, the model can generate interpolations of objects from its input distribution.

Artificial intelligence-based technologies can help ACA International members and other financial services providers better understand their customers' preferences and adapt their communications approaches, Vice President and Senior Counsel of Federal Advocacy Leah Dempsey said in recent comments on five federal agencies' request for information (RFI) on artificial intelligence. The Federal Reserve Board, Consumer Financial Protection Bureau, the Federal Deposit Insurance Corporation, the National Credit Union Administration and the Office of the Comptroller of the Currency announced RFI to gain input from financial institutions, trade associations, consumer groups and other stakeholders on the growing use of artificial intelligence by financial institutions, ACA previously reported. More specifically, the agencies sought comments to better understand the use of artificial intelligence, including machine learning, by financial institutions; appropriate governance, risk management and controls over artificial intelligence; challenges in developing, adopting and managing artificial intelligence; and whether any clarification would be helpful. Overall, ACA appreciates the agencies' recognition that artificial intelligence technologies, including voice recognition and natural language processing, can enhance consumer experiences and provide other benefits. "They can also help financial institutions provide products and services that are more customized for consumers and reveal patterns in consumer preferences," Dempsey said.

NASA has started nurturing AI technologies, from multiple Silicon Valley tech giants like Google, IBM and Intel for further enhancement in space science. NASA is focused on levelling up the ante to study and research more about life in outer space with Artificial Intelligence in the nearby future. Leveraging Artificial Intelligence can provide some amazing unknown data for accurate prediction of the behaviour of the universe. Silicon Valley tech giants are world-known for their constant innovation in Artificial Intelligence to enhance the traditional work system efficiently and effectively. Thus, NASA is partnering with these reputed companies to apply advanced machine learning algorithms for solving complex universal problems.