For our latest expert interview on our blog, we've welcomed Dhonam Pemba to share his thoughts on the topic of artificial intelligence (AI) and his journey behind founding KidX AI. Dhonam is a neural engineer by PhD, a former rocket scientist and a serial AI entrepreneur with one exit. He was CTO of the exited company, Kadho which was acquired by Roybi for its Voice AI technology. At Kadho Sports he was their Chief Scientist which had clients in MLB, USA Volleyball, NFL, NHL, NBA, and NCAA. His latest company, KidX, is in the AI edtech space, where he has built NLP and Voice assessment to serve China's leading robotics company with 4M users.

Learning good representations on multi-relational graphs is essential to knowledge base completion (KBC). In this paper, we propose a new self-supervised training objective for multi-relational graph representation learning, via simply incorporating relation prediction into the commonly used 1vsAll objective. The new training objective contains not only terms for predicting the subject and object of a given triple, but also a term for predicting the relation type. We analyse how this new objective impacts multi-relational learning in KBC: experiments on a variety of datasets and models show that relation prediction can significantly improve entity ranking, the most widely used evaluation task for KBC, yielding a 6.1% increase in MRR and 9.9% increase in Hits@1 on FB15k-237 as well as a 3.1% increase in MRR and 3.4% in Hits@1 on Aristo-v4. Moreover, we observe that the proposed objective is especially effective on highly multi-relational datasets, i.e. datasets with a large number of predicates, and generates better representations when larger embedding sizes are used.

Japanese-American scientist Syukuro Manabe, Klaus Hasselmann of Germany and Giorgio Parisi of Italy on Tuesday won the Nobel Physics Prize for climate models and the understanding of physical systems. The Nobel committee said it was sending a message with its prize announcement just weeks before the COP26 climate summit in Glasgow, as the rate of global warming sets off alarm bells around the world. "The world leaders that haven't got the message yet, I'm not sure they will get it because we are saying it," said Thor Hans Hansson, chair of the Nobel Committee for Physics. "But … what we are saying is that the modeling of climate is solidly based in physics theory." Manabe, 90, and Hasselmann, 89, will share half of the 10 million kronor ($1.1 million) prize for their research on climate models.

In this episode of the McKinsey Global Institute's Forward Thinking podcast, host Michael Chui speaks with Jeffrey Ding, researcher and founder of the ChinAI Newsletter, about information asymmetry in artificial intelligence between China and the West. They cover why data may not be like oil, the Chinese industry adage on products, platforms, and standards, "unsexy AI," and more. An edited transcript of this episode follows. Subscribe to the series on Apple Podcasts, Google Podcasts, Spotify, Stitcher, or wherever you get your podcasts. Anna Bernasek, co-host: Michael, there's a lot of talk right now about artificial intelligence, or AI, and what it means for global competition. I'm really glad we've got a guest today that can talk to us about what's really going on, particularly when it comes to the US and China. It definitely is a fascinating topic--at least, I find it personally. I'm a former AI practitioner and more recently, at the McKinsey Global Institute, have been able to study the impact of AI on business and more broadly. And one of the reasons I'm so excited about today's conversation is because it's with somebody you probably don't know yet but probably should. He's famous in certain corners of the internet but his work, it turns out, is relevant everywhere.

Yann Andre LeCun, a French computer scientist who focuses on machine learning, computer vision, mobile robotics, and computational neuroscience, recently tweeted that one of his articles has been rejected from NeurIPS 2021. Yann LeCun is a Silver Professor at New York University's Courant Institute of Mathematical Sciences and Vice President, Chief AI Scientist at Facebook. He is well-known for his work on optical character recognition and computer vision using convolutional neural networks (CNNs) and is often regarded as the inventor of convolutional nets. He is also a co-creator of the DjVu image compression technology. The author is a multifaceted individual with academic and industrial experience in artificial intelligence, machine learning, deep learning, computer vision, intelligent data analysis, data mining, data compression, digital library systems, and robotics.

DeepMind's AlphaFold represents the first time a significant scientific problem has been solved by ... [ ] AI. It can be difficult to distinguish between substance and hype in the field of artificial intelligence. In order to stay grounded, it is important to step back from time to time and ask a simple question: what has AI actually accomplished or enabled that makes a difference in the real world? This summer, DeepMind delivered the strongest answer yet to that question in the decades-long history of AI research: AlphaFold, a software platform that will revolutionize our understanding of biology. In 1972, in his acceptance speech for the Nobel Prize in Chemistry, Christian Anfinsen made a historic prediction: it should in principle be possible to determine a protein's three-dimensional shape based solely on the one-dimensional string of molecules that comprise it. Finding a solution to this puzzle, known as the "protein folding problem," has stood as a grand challenge in the field of biology for half a century.

Robotic grasping and manipulation has historically been dominated by rigid grippers, force/form closure constraints, and extensive grasp trajectory planning. The advent of soft robotics offers new avenues to diverge from this paradigm by using strategic compliance to passively conform to grasped objects in the absence of active control, and with minimal chance of damage to the object or surrounding environment. However, while the reduced emphasis on sensing, planning, and control complexity simplifies grasping and manipulation tasks, precision and dexterity are often lost. This talk will discuss efforts to increase the robustness of soft grasping and the dexterity of soft robotic manipulators, with particular emphasis on grasping tasks that are challenging for more traditional robot hands. This includes compliant objects, thin flexible sheets, and delicate organisms.

Gil's thesis research was focused on 3D Computer Vision and has been published at CVPR, the top computer vision research conference in the world. Datagen is a pioneer in the new field of Simulated Data, a subset of synthetic data, which concentrates on photo-realistically recreating the world around us. The company launched from stealth with over $18M in funding in March 2021 and is now working with a number of Fortune 100 companies in augmented/virtual reality, robotics, and automotive, including the majority of the top U.S. tech giants. What initially attracted you to robotics and machine learning? Sci-Fi books, like Isaac Asimov's Foundation Series and iRobot always got me thinking about a future in which robots were an integral part of our day-to-day lives.

On Wednesday's show we talked about Tesla's full self-driving mode, which it is about to make available to more drivers. And yes, the name implies that the cars will drive themselves. A human will still have to be in control. And that's where we are right now with self-driving cars. They might help you drive, but they might also make a mistake that causes an accident if you're not paying attention.

Five graduate students at Carnegie Mellon University's School of Computer Science have received Siebel Scholars awards for 2022. "Every year, the Siebel Scholars continue to impress me with their commitment to academics and influencing future society. This year's class is exceptional, and once again represents the best and brightest minds from around the globe who are advancing innovations in healthcare, artificial intelligence, financial services and more," said Thomas M. Siebel, chairman of the Siebel Scholars Foundation. "It is my distinct pleasure to welcome these students into this ever-growing, lifelong community, and I personally look forward to seeing their impact and contributions unfold." Ahuja is a Ph.D. candidate in the Human-Computer Interaction Institute (HCII) whose research focuses on machine learning and sensing.