Scaling real robot data is a key bottleneck in imitation learning, leading to the use of auxiliary data for policy training. While other aspects of robotic manipulation such as image or language understanding may be learned from internet-based datasets, acquiring motion knowledge remains challenging. Human data, with its rich diversity of manipulation behaviors, offers a valuable resource for this purpose. While previous works show that using human data can bring benefits, such as improving robustness and training efficiency, it remains unclear whether it can realize its greatest advantage: enabling robot policies to directly learn new motions for task completion. In this paper, we systematically explore this potential through multi-task human-robot cotraining. We introduce MotionTrans, a framework that includes a data collection system, a human data transformation pipeline, and a weighted cotraining strategy. By cotraining 30 human-robot tasks simultaneously, we direcly transfer motions of 13 tasks from human data to deployable end-to-end robot policies. Notably, 9 tasks achieve non-trivial success rates in zero-shot manner. MotionTrans also significantly enhances pretraining-finetuning performance (+40% success rate). Through ablation study, we also identify key factors for successful motion learning: cotraining with robot data and broad task-related motion coverage. These findings unlock the potential of motion-level learning from human data, offering insights into its effective use for training robotic manipulation policies. All data, code, and model weights are open-sourced https://motiontrans.github.io/.

Understanding human tasks through video observations is an essential capability of intelligent agents. The challenges of such capability lie in the difficulty of generating a detailed understanding of situated actions, their effects on object states (\ie, state changes), and their causal dependencies. These challenges are further aggravated by the natural parallelism from multi-tasking and partial observations in multi-agent collaboration. Most prior works leverage action localization or future prediction as an \textit{indirect} metric for evaluating such task understanding from videos. To make a \textit{direct} evaluation, we introduce the EgoTaskQA benchmark that provides a single home for the crucial dimensions of task understanding through question answering on real-world egocentric videos.

Opinion If the soap opera of Microsoft's relationship with open source had a theme tune, it'd be "The Long and Winding Goad". To a company whose entire existence depended on market control, open source's radical freedoms were an existential, cancerous threat. In return, open source was only too happy to play the upstart punk movement to Microsoft's bloated prog rock. In the end, both sides accepted the inevitable. Redmond wasn't going to control the cloud and mobile the way it controlled business IT, and the cloud and mobile loved open source. Interoperability was more profitable than insults.

Anxiety about automation is prevalent in this era of rapid technological advances, especially in artificial intelligence (AI), machine learning (ML), and robotics. Accordingly, how human labor competes, or cooperates, with machines in performing a range of tasks (what we term "the race between human labor and machines") has attracted a great deal of attention among the public, policymakers, and researchers.14,15,18 While there have been persistent concerns about new technology and automation replacing human tasks at least since the Industrial Revolution,8 recent technological advances in executing sophisticated and complex tasks--enabled by a combinatorial innovation of new techniques and algorithms, advances in computational power, and exponential increases in data--differentiate the 21st century from previous ones.14 For instance, recent advances in autonomous self-driving cars demonstrate the way a wide range of human tasks that have been considered least susceptible to automation may no longer be safe from automation and computerization. Another case in point is human competition against machines, such as IBM's Watson on the TV game show "Jeopardy!" Both cases imply that some tasks, such as pattern recognition and information processing, are being rapidly computerized. Furthermore, recent studies suggest that robotics also plays a role in automating manual tasks and decreasing employment of low-wage workers.3,22

One of the only lucid thought experiments ever carried out by econometricians, the "red bus-blue bus" problem illustrates a central drawback that comes with using statistical estimation to quantify the probability a person makes a specific choice when faced with several alternatives. As the thought experiment goes, imagine that you're indifferent between taking either a car or a red bus to work. Owing to your indifference, an estimate of your probability of picking either option is a coin flip. There is a 50 percent chance that you're taking the car and 50 percent that you're taking the red bus. Thus, your odds of selection are one-to-one.

Anxiety, stress, overthinking, and trauma are commonly used words to describe people suffering from mental health disorders that appear from work overload, depression, negative feedback, and much more. It's very likely to see people suffering from anxiety faster than coming in contact with positive and uplifting people. Here is how to treat your stress with AI technology. The fact is, the world isn't becoming a safer or stable place. Nevertheless, technology hasn't given up on humankind yet. Researchers and scientists are doing their best to provide aid and stabilize the situation for the greater good.

Finally, AI is ready for the mainstream. When your enterprise is handling transactions between 25 million sellers and 182 million buyers, supporting 1.5 billion listings, manual decision-making processes just won't cut. Such is the case with eBay, the mega commerce site, that has been employing artificial intelligence for more than a decade. As Forbes contributor Bernard Marr points out, eBay employs AI across a broad range of functions, "in personalization, search, insights, discovery and its recommendation systems along with computer vision, translation, natural language processing and more." As part of a massive operation with so much experience with AI, Mazen Rawashdeh, CTO of eBay, has plenty to say about the current state of enterprise AI.

He commented: "We have parts of our human nature which were ok in early evolutionary times, they probably had an advantage in early evolutionary times but which now are terrible dangers to us. "They include our tendency to focus on the short term, our tendency to be wary and fearful of those who are different from us – it made good sense probably in previous social times but now that instinct is often detrimental. "We have a tendency towards a sweet tooth which probably made a lot of sense when there wasn't much sugar around but now when we have got most of the time lots of sugar available is no longer the best aspect of human nature. "So we have to figure out how do we control these tendencies before we allow others to magnify these impulses with the horrific technology that we have today and produce devastating consequences."

If this week taught us anything, it's that the brilliant shine around A.I. may finally be starting to fade. Investors are starting to realize that the reality of running an A.I. business is more than "train an algorithm, scale infinitely, profit." VCs at Andreessen Horowitz wrote this week that A.I. startups could be tougher than typical software investments: In addition to huge investments into cloud computing that many tech startups must make, A.I startups have the additional, hidden costs of labelling data, or the human task of looking at a picture, determining it's a picture of a bird, and typing the word "bird." Oh yeah, and there's the privacy concerns too -- see Alexa, Google Assistant, etc. And even when the initial algorithm is off the ground, exporting it to make it work in other locations or countries means labelling all over again in other languages.

Businesses that have automated important functions are often the ones that experience the most growth and the most success - as well as having the happiest customers. This may have been true for some time, but as we enter an age of digital transformation in 2020 and beyond, it gets even more valuable. As your business automates more, then business runs smoother and is hopefully able reach more customers, as technology becomes an enabler of automation instead of a constant inhibitor or roadblock to success. Workflow automation can adjust and adapt to technology trends as opposed to resisting the changes and improvements - so here's our guide to the technology. Before explaining how workflow automation is changing and evolving, and how it is impacting every aspect of a company, here's a short review of how it works.