A lot of people are worried about the prospect of competing with AI for their jobs, but this probably isn't what they were expecting. The FBI has warned of an uptick in cases where "deepfakes" and stolen personal information are being used to apply for jobs in the U.S. -- including faking video interviews. The shift to remote work is great news for lots of people, but like any other change in methods and expectations it is also a fresh playground for scammers. Security standards are being updated, recruiters are adapting, and of course the labor market is wild enough that hiring companies and applicants both are trying to move faster than ever. In the midst of these ongoing changes, today's FBI public service announcement warns that deepfakes are once again being employed for nefarious purposes -- in this case imitating people whose identities have been stolen to apply for jobs: Complaints report the use of voice spoofing, or potentially voice deepfakes, during online interviews of the potential applicants.

British-born Andrew Ng has had a rich career in the technology industry as Co-Founder and Head of Google Brain, former Chief Scientist at Baidu and Co-Founder of Coursera. At Baidu, Ng built the company's artificial intelligence (AI) sector into a team of several people. In an interview with Lex Fridman, Ng shared where his passion for the industry started: " Growing up in Hong Kong and Singapore, I started learning to code when I was five or six years old. At that time I was learning the BASIC programming language and they would take these folks and they'll tell you type this program into your computer." "So I typed out programs on my computer and as the result of all the typing, I would get to play these very simple, shoot them up games that I had implemented on my little computer. So I thought it was fascinating as a young kid that I could write this code. I was really just copying code from a book into my computer to then play these cool little video games. Another moment for me was when I was a teenager and my father was a doctor was reading about expert systems and about neural networks. So he got me to read some of these books and I thought it was really cool that you could write a computer that started to exhibit intelligence." he continued.

The London Marriott Hotel Grosvenor Square was the venue for the SC Media Awards 2022, the cybersecurity industry's coveted and prestigious awards ceremony on June 21. Vectra, a leader in AI-based cyber threat detection and response for hybrid and multi-cloud enterprises, won the "Excellence in Threat Solutions Award" in the "Best Enterprise Behavioral Analysis and Threat Detection" category for its Vectra AI platform. Vectra didn't just win that title, however, as it was also ranked at the event as "Highly Commended" in the "Best Use of Machine Learning and Artificial Intelligence", "Best Customer Service" and "Best Security Company" categories. Founded in 2010 and based in San Jose, California, Vectra is a leader in threat detection and response for hybrid and multi-cloud enterprises. Its Vectra AI platform uses AI to quickly detect threats in the public cloud, identity, SaaS applications and data centers.

Welcome to our June 2022 monthly digest, where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. This month, we find out about meta-learning, explore the importance of images in communicating about AI, and ponder over who to vote for in the AI Song Contest. In the latest episode of New voices in AI, Oumaima Hajri shares her work and journey in ethical AI. Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, and Satinder Singh won an ICLR 2022 outstanding paper award for their work Bootstrapped meta-learning. We spoke to Sebastian about how the team approached the problem of meta-learning, how their algorithm performs, and plans for future work.

Equity is not the same principle as equality. Within the social context they both relate to fairness; equality means treating everyone the same regardless of need, while equity means treating people differently depending on their needs. Consider vaccinations, if we based public health policy on equality, perhaps there would be a lottery system to decide who gets vaccinated first, giving everyone an equal chance. In practice, however, vaccinations are prioritized based on equity, those with the greatest risk, frontline healthcare workers and the elderly, understandably, are first in line. Assuming we understand the causal relationship between treatment and outcome, the question then is, how do we identify the subgroups who experience the greatest average causal effects, whether positive or negative.

Star Wars is one of the most popular sci-fi brands in history and has inspired and captivated generations of fans. Can this feat be repeated? I believe the booming Web 3.0 culture, supported by NFTs can be a launchpad for a new mega-hit cultural sci-fi brand. NFT (Non-Fungible Token) is a new world that lots of people are not familiar with. Imagine the Internet in the early 1990s.

LaMDA is a software program that runs on Google TPU chips. Like the classic brain in a jar, some would argue the code and the circuits don't form a sentient entity because none of it engages in life. Google engineer Blake Lemoine caused controversy last week by releasing a document that he had circulated to colleagues in which Lemoine urged Google to consider that one of its deep learning AI programs, LaMDA, might be "sentient." Google replied by officially denying the likelihood of sentience in the program, and Lemoine was put on paid administrative leave by Google, according to an interview with Lemoine by Nitasha Tiku of The Washington Post. There has been a flood of responses to Lemoine's claim by AI scholars. University of Washington linguistics professor Emily Bender, a frequent critic of AI hype, told Tiku that Lemoine is projecting anthropocentric views onto the technology. "We now have machines that can mindlessly generate words, but we haven't learned how to stop imagining a mind behind them," Bender told Tiku. In an interview with MSNBC's Zeeshan Aleem, AI scholar Melanie Mitchell, Davis Professor of Complexity at the Santa Fe Institute, observed that the concept of sentience has not been rigorously explored. Mitchell concludes the program is not sentient, however, "by any reasonable meaning of that term, and the reason is because I understand pretty well how the system works."

During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as Complementary Metal-Oxide-Semiconductor (CMOS), in power and area-constrained settings remains a challenging task; especially when many recording channels are used. In this paper, we propose a novel low-latency parallel Convolutional Neural Network (CNN) architecture that has between 2-2,800x fewer network parameters compared to SOTA CNN architectures and achieves 5-fold cross validation accuracy of 99.84% for epileptic seizure detection, and 99.01% and 97.54% for epileptic seizure prediction, when evaluated using the University of Bonn Electroencephalogram (EEG), CHB-MIT and SWEC-ETHZ seizure datasets, respectively. We subsequently implement our network onto analog crossbar arrays comprising Resistive Random-Access Memory (RRAM) devices, and provide a comprehensive benchmark by simulating, laying out, and determining hardware requirements of the CNN component of our system. To the best of our knowledge, we are the first to parallelize the execution of convolution layer kernels on separate analog crossbars to enable 2 orders of magnitude reduction in latency compared to SOTA hybrid Memristive-CMOS DL accelerators. Furthermore, we investigate the effects of non-idealities on our system and investigate Quantization Aware Training (QAT) to mitigate the performance degradation due to low ADC/DAC resolution. Finally, we propose a stuck weight offsetting methodology to mitigate performance degradation due to stuck RON/ROFF memristor weights, recovering up to 32% accuracy, without requiring retraining. The CNN component of our platform is estimated to consume approximately 2.791W of power while occupying an area of 31.255mm$^2$ in a 22nm FDSOI CMOS process.

Now senior director for AI and legal ethics at NVIDIA, Pope spends her days working with internal teams across the company to ensure its products engender trust across industries. In a recent "Solving for Tech Ethics" podcast, Pope joined Beena Ammanath, Deloitte LLP's Trustworthy and Ethical Technology leader, to discuss the challenges and opportunities associated with creating trustworthy AI. Ammanath: Five or 10 years ago, roles like yours just didn't exist. What does a day in your job look like? Pope: One day does not look like the next. Take yesterday as an example.

We all will collectively accept the truth that nowadays almost everyone is selling their software by saying that it contains AI and it is really very popular amongst everyone. It's like if you want to start a discussion about any IT topic, the hot topic you find there will be AI. And everyone loves to speak about it confidently and pour out their own self-imagined concepts, which are mostly nowhere near to reality, though it's good that people are taking interest in such topics. Also, I will try not to be too much technical and will explain most of the things in Layman's Terms. If you want a more detailed and technical article about AI you can check my other article.