Reinforcement learning competition pushes the boundaries of embodied AI

In a recent development in embodied AI, scientists at IBM, the Massachusetts Institute of Technology, and Stanford University developed a new challenge that will help assess AI agents’ ability to find paths, interact with objects, and plan tasks efficiently. Titled ThreeDWorld Transport Challenge, the test is a virtual environment that will be presented at the Embodied AI Workshop during the Conference on Computer Vision and Pattern Recognition, held online in June.

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Waymo’s leadership shift spotlights self-driving car challenges

Waymo leadership

Waymo, Alphabet’s self-driving car subsidiary, has reshuffled its top executive lineup. John Krafcik, Waymo’s CEO since 2015, announced on April 2 that he would be stepping down from his role. Krafcik is being replaced by former COO Tekedra Mawakana and former CTO Dmitri Dolgov and will remain as an advisor to the company.

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[Book Review] The challenges of applied machine learning

Real world AI

Every year, machine learning researchers fascinate us with new discoveries and innovations. There are a dozen artificial intelligence conferences where researchers push the boundaries of science and show how neural networks and deep learning architectures can take on new challenges in areas such as computer vision and natural language processing. But using machine learning in real-world applications and business problems—often referred to as “applied machine learning” or “applied AI”—presents challenges that are absent in academic and scientific research settings.

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Adversarial training reduces safety of neural networks in robots: Research

There’s a growing interest in employing autonomous mobile robots in open work environments such as warehouses, especially with the constraints posed by the global pandemic. And thanks to advances in deep learning algorithms and sensor technology, industrial robots are becoming more versatile and less costly.

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Why AI struggles to grasp cause and effect

Robot scientist

When you look at the following short video sequence, you can make inferences about causal relations between different elements. For instance, you can see the bat and the baseball player’s arm moving in unison, but you also know that it is the player’s arm that is causing the bat’s movement and not the other way around. You also don’t need to be told that the bat is causing the sudden change in the ball’s direction.

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How ‘MAB’ (Multi-Armed Bandit), a Reinforcement Learning Algorithm, helps to solve ad Optimization problem

Graph on background

Every day, digital advertisement agencies serve billions of ads on news websites, search engines, social media networks, video streaming websites, and other platforms. And they all want to answer the same question: Which of the many ads they have in their catalog is more likely to appeal to a certain viewer? Finding the right answer to this question can have a huge impact on revenue when you are dealing with hundreds of websites, thousands of ads, and millions of visitors.

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How reinforcement learning chooses the ads you see

Every day, digital advertisement agencies serve billions of ads on news websites, search engines, social media networks, video streaming websites, and other platforms. And they all want to answer the same question: Which of the many ads they have in their catalog is more likely to appeal to a certain viewer? Fortunately (for the ad agencies, at least), reinforcement learning,…

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Apple’s self-driving car strategy may be stuck in neutral

KIA Motors factory

After weeks of confusion and contradictory news, it has almost become confirmed that Hyundai-Kia will start manufacturing Apple’s mysterious self-driving electric vehicle at one of its factories in 2024. Or later. Or not at all.
As is with nearly all news coming from Apple, the report is shrouded in secrecy, unconfirmed facts, and lots of quotes from anonymous sources. But this latest news does have some key points that, if true, paint a clearer picture about Apple’s self-driving plans — and leave many more questions unanswered about the future of the company.

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Case studies of successful AI startups

With tech giants pouring billions of dollars into artificial intelligence projects, it’s hard to see how startups can find their place and create successful business models that leverage AI. However, while fiercely competitive, the AI space is also constantly causing fundamental shifts in many sectors. And this creates the perfect environment for fast-thinking and -moving startups to carve a niche for themselves before the big players move in.

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A new technique called ‘concept whitening’ promises to provide neural network interpretability

Deep neural networks can perform wonderful feats, thanks to their extremely large and complicated web of parameters. But their complexity is also their curse: The inner workings of neural networks are often a mystery — even to their creators. This is a challenge that has been troubling the artificial intelligence community since deep learning started to become popular in the early 2010s.

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Is neuroscience the key to protecting AI from adversarial attacks?

Stop sign

Deep learning has come a long way since the days when it could only recognize handwritten characters on checks and envelopes. Today, deep neural networks have become a key component of many computer vision applications, from photo and video editors to medical software and self-driving cars. Roughly fashioned after the structure of the brain, neural networks have come closer to seeing the world as humans do. But they still have a long way to go, and they make mistakes in situations where humans would never err.

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How to (not) write an AI pitch

AI Pitch

These are exciting times for the artificial intelligence community. Interest in the field is growing at an accelerating pace, registration at academic and professional machine learning courses is soaring, attendance in AI conferences is at an all-time high, and AI algorithms have become a vital component of many applications we use every day.

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Leading computer scientists debate the next steps for AI in 2021

The 2010s were huge for artificial intelligence, thanks to advances in deep learning, a branch of AI that has become feasible because of the growing capacity to collect, store, and process large amounts of data. Today, deep learning is not just a topic of scientific research but also a key component of many everyday applications. But a decade’s worth of research and application has made it clear that in its current state, deep learning is not the final solution to solving the ever-elusive challenge of creating human-level AI.

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DeepMind’s annual report: Why it’s hard to run a commercial AI lab

Google

A tech company losing money is nothing new. The tech industry is replete with examples of companies who burned investor money long before becoming profitable. But DeepMind is not a normal company seeking to grab a share of a specific market. It is an AI research lab that has had to repurpose itself into a semi-commercial outfit to ensure its survival.

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The fate of Boston Dynamics

Boston Dynamics

Last week, Hyundai officially announced the much-anticipated deal to acquire a controlling interest in famous robotics company Boston Dynamics. According to a joint press release by the two companies, Hyundai will buy an 80-percent stake in Boston Dynamics, and SoftBank, the previous owner of the robotics company, will retain 20 percent ownership after the transaction is completed in June 2021. While details have yet to be revealed, the deal puts Boston Robotics at a $1.1 billion valuation.

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Why it’s a great time to be a data scientist at a big company

Coded graph

One of the common characteristics among longstanding companies that have managed to survive and thrive in the age of constantly evolving technologies is the correct use of data science, data engineering, and machine learning. The ubiquity of internet connectivity and advances in artificial intelligence are providing organizations with unprecedented potential to capture value, learn, and develop solutions that solve real problems.

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