You may have seen some headlines about this wild artificial intelligence chatbot, ChatGPT. We asked it some questions about FCPA compliance to see if it's worth the hype. You might want to be seated for this. ChatGPT was developed by OpenAI, a company founded and funded in part by Elon Musk. Earlier this year, we reviewed a predecessor to ChatGPT called GPT-3.

You're about to confront a pernicious new challenge that is spreading, kudzu-like, into your student writing assignments: papers augmented with artificial intelligence. The first online article generator debuted in 2005. Now, A.I.-generated text can now be found in novels, fake news articles and real news articles, marketing campaigns, and dozens of other written products. The tech is either free or cheap to use, which places it in the hands of anyone. Using an A.I. program is not "plagiarism" in the traditional sense--there's no previous work for the student to copy, and thus no original for teachers' plagiarism detectors to catch.

Regulators have opened an investigation into Facebook amid concerns it is using its vast troves of personal data to push its own shopping and data tools. The probe by the UK's competition regulator will examine whether it is abusing its dominant position in online advertising. It comes amid growing antitrust concerns about the way many technology companies – not just Facebook but others such as Apple – have been able to use their vast size and hold on the market to unfairly benefit themselves. The Competition and Markets Authority (CMA) will look into how the social network gathers and uses certain data and whether it may provide an unfair advantage over rivals in the online classified ads and online dating space. As well as Facebook's advertising services, Facebook Login, a feature that allows people to sign into other websites and apps, will also form part of the probe.

At the center of the growing digital economy is data. Data is to the 21st century what oil was to the 20th century. In every industry, it are the companies that can use data effectively that succeed. And investing is no different. In their search for alpha generating ideas, investment managers are increasingly turning to sources of alternative financial data. But what is alternative data and how does it give fund managers an edge? The returns generated by investors can be classified as either alpha or beta.

When a virtually unknown, 14-year-old "Fortnite" competitor playing under the name sF Roller won a solo "Fortnite" Championship Series in May, earning $25,000 and shocking his mother, he had an important announcement to tweet: "thank you aim assist." Aim assist is a feature enabled for players who use controllers -- as opposed to a mouse and a keyboard -- that helps guide the crosshairs toward opponents automatically. It is added to compensate for the fact that aiming with a thumbstick is more difficult than with a mouse, a far more accurate tool. Roller's victory, aided by aim assist, set off a powder keg. After May's tournament, "Fortnite" pros and commentators heaped insults on players who use controllers.

Simplicity is the ultimate sophistication. Differentiable Architecture Search (DARTS) has now become one of the mainstream paradigms of neural architecture search. However, it largely suffers from several disturbing factors of the optimization process whose results are unstable to reproduce. Chu et al. [2019a] point out that skip connections natively have an unfair advantage in exclusive competition which primarily leads to dramatic performance collapse. While Fair-DARTS (Chu et al. [2019a]) turns the unfair competition into a collaborative one, we instead impede such unfair advantage by injecting unbiased random noise into skip operations' output. In effect, the optimizer should perceive this difficulty at each training step and refrain from overshooting on skip connections, but in a long run, it still converges to the right solution area since no bias is added to the gradient in terms of expectation. We name this novel approach as NoisyDARTS. Our experiments on CIFAR-10 and ImageNet attest that it can effectively break the skip connections' unfair advantage and yield better performance. It generates a series of models that achieve state-of-the-art results on both datasets.

Differential Architecture Search (DARTS) is now a widely disseminated weight-sharing neural architecture search method. However, there are two fundamental weaknesses remain untackled. First, we observe that the well-known aggregation of skip connections during optimization is caused by an unfair advantage in an exclusive competition. Second, there is a non-negligible incongruence when discretizing continuous architectural weights to a one-hot representation. Because of these two reasons, DARTS delivers a biased solution that might not even be suboptimal. In this paper, we present a novel approach to curing both frailties. Specifically, as unfair advantages in a pure exclusive competition easily induce a monopoly, we relax the choice of operations to be collaborative, where we let each operation have an equal opportunity to develop its strength. We thus call our method Fair DARTS. Moreover, we propose a zero-one loss to directly reduce the discretization gap. Experiments are performed on two mainstream search spaces, in which we achieve new state-of-the-art networks on ImageNet. Our code is available on https://github.com/xiaomi-automl/fairdarts.

I don't do Fantasy Football but if I did, I would probably look to #AI to give me an unfair advantage. Winning in Fantasy Football with help of AI? See how the experts @IBM built ESPN's Fantasy Football Application at the Digital Developer Conference: AI & Cloud https://lnkd.in/eh4nY4a

One reason for the widespread interest in AI is that it has the potential to reduce the degree of bias underpinning human decisions. For example, meta-analytic studies have long highlighted the pervasive nature of bias in hiring and recruitment. Even in the rich and liberal world, there are many biases at play in the workplace, which account for the unmeritocratic or unfair advantage that some groups have over others, irrespective of their actual talent or potential: sexism, racism, and ageism, to name just a few. But one of the most prominent biases is hardly ever discussed or acknowledged, namely the beauty bias -- also known as "lookism." Indeed, the existence of a beauty premium in the labor market is well-documented.

"Self-aware robots with Artificial General Intelligence will destroy humanity." We all have heard this version of artificial intelligence (AI) dystopia in science fiction, movies or even from respected scientists and philosophers. But I argue a completely different version of AI dystopia could arrive much sooner. So soon, it could be within our or our children's lifetime. Unfortunately, most of us are distracted by the binary thinking of machine versus human, bedazzled every time some new technological capacities are achieved, and neglect to pay thoughtful attention to this forthcoming unfolding crisis.