In recent years, cybercrime has reached epidemic proportions, with far ranging impacts across the business world. Cyberattacks pose a monumental threat with major developments: attacks have become far more sophisticated and they have exponentially increased in volume. Better executed than ever before, the UN estimates that 80 per cent of all cyberattacks are carried out by technologically-advanced criminal organisations that share data, tools, and expertise. By 2021, it is estimated that cybercrime will cost the global economy over $2 trillion. This makes it imperative for companies to make concerted efforts to improve their cybersecurity health, and evolve from a compliance-focused approach to a more threat-aware strategy focusing on risk.
We've seen inappropriate and unintended bias emerge from various industries' use of AI, including recruiting and mortgage lending. In those cases, flawed outcomes were evident as bias was reflected in ways that relate to distinct features of our identity: gender, race, age. But I've spent a lot of time thinking about areas in which we don't even realize AI bias is present. In a complex field like cybersecurity, how do we recognize biased outcomes? AI has become a prime security tool, with research indicating that 69% of IT executives saying they can't respond to threats without AI.
Experts say Iran may retaliate for the killing of Qassem Soleimani, its top military leader, with cyber attacks on American companies. Experts say Iran may retaliate for the killing of Qassem Soleimani, its top military leader, with cyber attacks on American companies. Cybersecurity researchers and U.S. government officials said hackers linked to Iran are probing American companies for vulnerabilities. The warnings suggest that the next phase of hostilities between the U.S. and Iran, following the Jan. 3 killing of a top Iranian general in an American drone strike, is likely to play out in cyberspace. The Iranian regime is accused of being behind some high-profile online operations against American targets in recent years.
Asynchronous stochastic approximations are an important class of model-free algorithms that are readily applicable to multi-agent reinforcement learning (RL) and distributed control applications. When the system size is large, the aforementioned algorithms are used in conjunction with function approximations. In this paper, we present a complete analysis, including stability (almost sure boundedness) and convergence, of asynchronous stochastic approximations with asymptotically bounded biased errors, under easily verifiable sufficient conditions. As an application, we analyze the Policy Gradient algorithms and the more general Value Iteration based algorithms with noise. These are popular reinforcement learning algorithms due to their simplicity and effectiveness. Specifically, we analyze the asynchronous approximate counterpart of policy gradient (A2PG) and value iteration (A2VI) schemes. It is shown that the stability of these algorithms remains unaffected when the approximation errors are guaranteed to be asymptotically bounded, although possibly biased. Regarding convergence of A2VI, it is shown to converge to a fixed point of the perturbed Bellman operator when balanced step-sizes are used. Further, a relationship between these fixed points and the approximation errors is established. A similar analysis for A2PG is also presented.
Nearly half of all Americans are affected by a cyber security breach at Equifax, one of the nation's three major credit-reporting agencies. Here's how to avoid being a victim. Two key U.S. senators Monday sought detailed information from Equifax about the cyberbreach that potentially compromised the personal information of 143 million U.S. consumers. Sen. Orrin Hatch, R-Utah, who chairs the Senate Committee on Finance, and Sen.Ron Wyden, D-Oregon, the panel's ranking minority member, asked the credit-reporting giant for a timeline of the breach, along with details of Equifax's efforts to quantify the scope of the intrusion and limit consumer harm. They also asked whether records related to the IRS, the Social Security Administration and the Centers for Medicare & Medicaid Services were compromised, and questioned Equifax about its cybersecurity protections and testing procedures.