Behind every successful scientific implementation, there is a theory that supports the results or allows one to anticipate the consequences. In the case of machine learning, however, the situation is a bit counterintuitive. Though the number of implementations of ML is spiking every day, one still cannot pinpoint the reason why a particular model is making some predictions. Machine learning models are called black-box models for a reason! Why does a certain model work?

Artificial intelligence, or AI, is one of these terms that is often misused and misunderstood. Artificial intelligence is an area that is continuously evolving, and as a result, people tend to lump related concepts like machine learning and deep learning into the same category. That being said, it's important to note that there are key differences between artificial intelligence and machine learning, despite their close relationship. If you've read anything about the companies leading the charge in artificial intelligence, you've probably noticed that machine learning and artificial intelligence are often mentioned together. It's been around in some conceptualized form or another for centuries now.

This week at the MLSys Conference in Austin, Texas, researchers from Rice University in collaboration with Intel Corporation, announced a breakthrough deep learning algorithm called SLIDE (sub-linear deep learning engine) that can rapidly train deep neural networks on CPUs (central processing units) and outperform GPUs (graphical processing units). This new deep learning technique is a potential game-changer for not only both hardware and AI software industries, but also any organization using deep learning. To understand why requires a bit of background knowledge of the role of GPUs in artificial intelligence (AI). GPUs have more logical cores than CPUs. The CPU is the brain of the computer where calculations are performed.

In 2011, IBM Watson, competed in the game show Jeopardy! It was a watershed moment not because the machine beat humans at their own game, but because the possibilities opened our eyes. What followed was a series of striking breakthroughs in AI -- image recognition, speech recognition, and many more -- all possible through a technique known as deep learning. As we step into a new decade, an entirely new picture of the future of AI emerges. Heading into a new decade, AI looks more like "AI-as-a-service," embedded into seemingly everything and almost invisible.

Tim has a passion for teaching and loves to teach about the world of machine learning and artificial intelligence.

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Google's DeepMind is putting its artificial intelligence systems to a new task: trying to figure out certain properties of the novel coronavirus which has killed thousands in the past couple of months. In a post Thursday, DeepMind (which was acquired by Google in 2014 and is now a subsidiary of Alphabet), said it has put its AlphaFold system to create "structure predictions of several under-studied proteins associated with SARS-CoV-2, the virus that causes COVID-19." These predictions haven't been experimentally verified, DeepMind says, but they may help scientists understand how the coronavirus functions. This, in turn, may be of use when developing a vaccine or cure. DeepMind says that understanding a protein's structure typically takes months or longer.

The internationally renowned Dan David Prize, headquartered at Tel Aviv University, annually awards three prizes of $1 million each to globally inspiring individuals and organizations. The total purse of $3 million makes the prize not only one of the most prestigious, but also one of the highest-value prizes internationally. Laureates are selected on the basis of their outstanding achievements and contributions in the year's chosen fields, each representing a time category. This year's fields are Cultural Preservation and Revival (Past category), Gender Equality (Present category) and Artificial Intelligence (Future category). Professor Amnon Shashua, co-founder and CEO of Mobileye, and Dr. Demis Hassabis, co-founder and CEO of DeepMind, have been named the 2020 Dan David Prize laureates in the field of artificial intelligence (AI).

As COVID-19 reaches more than 60 countries with global cases topping 95,500 and the number of death tolls crossing 3,000, the whole world is doing their best to avoid the catastrophe. While organisations like WHO and UN are releasing funds to facilitate research, many are looking towards AI to decelerate the crisis. The scientific communities across the world have galvanised to find a 21st-century solution to a 21st-century problem. Let's take a look at how AI is being used to contain the latest outbreak: DeepMind today have announced that they are releasing structure predictions of several proteins that can promote research into the ongoing research around COVID-19. They have used the latest version of AlphaFold system to find these structures.

Rice University computer scientists have overcome a major obstacle in the burgeoning artificial intelligence industry by showing it is possible to speed up deep learning technology without specialized acceleration hardware like graphics processing units (GPUs). Computer scientists from Rice, supported by collaborators from Intel, will present their results today at the Austin Convention Center as a part of the machine learning systems conference MLSys. Many companies are investing heavily in GPUs and other specialized hardware to implement deep learning, a powerful form of artificial intelligence that's behind digital assistants like Alexa and Siri, facial recognition, product recommendation systems and other technologies. For example, Nvidia, the maker of the industry's gold-standard Tesla V100 Tensor Core GPUs, recently reported a 41% increase in its fourth quarter revenues compared with the previous year. Rice researchers created a cost-saving alternative to GPU, an algorithm called "sub-linear deep learning engine" (SLIDE) that uses general purpose central processing units (CPUs) without specialized acceleration hardware.