This article shows how to get an incredibly fast per token throughput when generating with the 176B parameter BLOOM model. As the model needs 352GB in bf16 (bfloat16) weights (176*2), the most efficient set-up is 8x80GB A100 GPUs. The main reason for using these GPUs is that at the time of this writing they provide the largest GPU memory, but other GPUs can be used as well. For example, 24x32GB V100s can be used. Using a single node will typically deliver a fastest throughput since most of the time intra-node GPU linking hardware is faster than inter-node one, but it's not always the case.

Machine learning has many useful cybersecurity applications -- provided the technology behind it isn't unduly influenced or tampered with by malicious actors, such that its data integrity is sabotaged. Donnie Wendt, principal security researcher at Mastercard, calls this the "Uncle Ronnie" effect: "When my son was little, before he'd go over to visit my brother -- his Uncle Ronnie -- I'd say, 'Please, please, don't learn anything new from him.' Because I know he's going to teach my son something bad," Wendt told SC Media in an interview at the CyberRisk Alliance's 2022 InfoSec World Conference in Orlando, Florida. Likewise, an adversary can compromise a machine learning system -- teaching it bad habits that its operators must then undo -- if they can even detect the breach in the first place. On the cyber front, properly trained machine-learning systems can help with such tasks as classifying malware, identifying phishing attempts, intrusion detection, behavioral analytics, and predicting if and when a vulnerability will be exploited.

Why is speaking at WITS important to you? WITS events have been enjoyable for me as a speaker and attendee. I filled my schedule with panels related to technology's business and technical aspects. I appreciate that the programming is diverse in its approach to the topics covered under the Tech umbrella. What inspires me: The advancements associated with DALL*E, the AI system that creates realistic images and art from natural language descriptions, are astounding. There are implications for content, advertising, and investment opportunities.

Robots have come a long way from their origination to today where they are capable of doing almost any task. Robotics technology has changed the picture of the current world. Artificial Intelligence (AI) has come a long way, and now robots are more evolved than ever. AI-powered robots are based on the principles of human intelligence are defined as the way in which machines imitate human intelligence to perform simple and complex tasks. Like in any other industry, AI-powered robots have become an essential part of the warehousing industry as well.

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This article is part of a series where we walk step by step in solving fintech problems with Machine Learning using "All lending club loan data". In previous articles, we prepared a dataset and built a Logistic Regression model, and we discussed the most common "ML model evaluation metrics" for a classification problem in the fintech space. This article will try to "understand" how our model decision works and what packages can help us to answer this question. Machine learning models are frequently named "black boxes". They produce highly accurate predictions.

Today, machine learning helps determine the loan we qualify for, the job we get, and even who goes to jail. But when it comes to these potentially life-altering decisions, can computers make a fair call? In a study published September 29 in the journal Patterns, researchers from Germany showed that with human supervision, people think a computer's decision can be as fair as a decision primarily made by humans. "A lot of the discussion on fairness in machine learning has focused on technical solutions, like how to fix unfair algorithms and how to make the systems fair," says computational social scientist and co-author Ruben Bach of the University of Mannheim, Germany. "But our question is, what do people think is fair? They need to be accepted by society and meet normative beliefs in the real world."

Facebook parent company Meta Thursday announced Make a Video, an online tool that generates short movie clips based on a text description. Why it matters: Text-to-still-image AI systems, including DALL-E 2, Stable Diffusion, and rival projects from Google, Meta and others, have advanced with great speed over the past year. Now, Meta appears to have won the race to extend AI-based content creation to videos. Details: As with the still-image generators, all people have to do is describe something they want to see in a verbal prompt and the system returns a visual result -- in this case, a video. The big picture: AI image generators have wowed observers, but they also raise significant concerns regarding ownership, privacy, the data sets they're based on and the impact their advent may have on artists' income and employment.

For years, artist Steve Coulson wanted to make his own comic. "The problem has always been – I can't draw," he says. But in 2022, Coulson published a beautiful comic called Summer Island. The 40-page folk-horror story about a sea god festival features detailed illustrations with a coherent visual style-- all created with the help of artificial intelligence. As AI image generators, such as OpenAI's popular DALL-E and DALL-E2, become more widespread, some forecast the death of human artforms.