This highlights the complexity of human vision and agency. The next time you go to a supermarket, consider how easily you can find your way through aisles, tell the difference between different products, reach for and pick up different items, place them in your basket or cart, and choose your path in an efficient way. And you're doing all this without access to segmentation and depth maps and by reading items from a crumpled handwritten note in your pocket. Above: Experiments show hybrid AI models that combine reinforcement learning with symbolic planners are better suited to solving the ThreeDWorld Transport Challenge. The TDW-Transport Challenge is in the process of accepting submissions.
The #GirlsInAI2021 international hackathon by Teens in AI was attended by 83 teams composed of over 950 participants from 23 different countries. The first prize was won by a French team, composed of three teenagers aged 16 to 17. They designed Hear-Me, an application based on artificial intelligence for people with hearing loss. The Hear-Me app is an AI-based tool combined with a device that aims to improve communication and interaction for people with hearing loss in face-to-face situations. The platform aims to help hearing impaired people in particular contexts, such as the one that requires wearing a mask during the Covid-19 crisis.
Artificial intelligence – the ability of machines to use massive amounts of data and computing power to mimic such human attributes as reasoning – is transforming our world. But is higher education keeping up? The answer will play a major role in determining whether the United States will meet the challenge from China and elsewhere. We are well beyond the days when AI was limited to such science fiction as the famously petulant computer "Hal" in the film "2001: A Space Odyssey." AI now plays a major role in healthcare diagnostics and treatment, transportation, robotics, finance, entertainment, and in higher education itself.
COVID-19 ushered in major shifts in the way that healthcare professionals (HCPs) interact with life sciences companies, but how have emerging biopharma companies adapted to this new reality? The core challenges small- to mid-sized (SMB) companies face -- limited resources and overburdened staff tasked with a wide span of responsibilities -- have not changed. Yet suddenly, there is growing demand for advanced technology solutions that empower leaner commercial teams to reach more HCPs efficiently. The life sciences industry has been trying to find the right ratio of sales reps to HCPs for decades. Today, there are about 60,000 pharmaceutical sales reps in the U.S., down from 100,000 in the mid-2000s.
ColorShapeLinks is an AI competition for the Simplexity board game with arbitrary game dimensions. The first player to place n pieces of the same type in a row wins. In this regard, the base game, with a 6 x 7 board and n 4, is similar to Connect Four. However, pieces are defined not only by color, but also by shape: round or square. Round or white pieces offer the win to player 1, while square or red pieces do the same for player 2. Contrary to color, players start the game with pieces of both shapes.
In machine learning, bigger may not always be better. As the datasets and the machine learning models keep expanding, researchers are racing to build state-of-the-art benchmarks. However, larger models can be detrimental to the budget and the environment. Over time, researchers have developed several ways to shrink the deep learning models while optimizing training datasets. In particular, three techniques–pruning, quantization, and transfer learning–have been instrumental in making models run faster and more accurately at lesser compute power.
The modern AI revolution began during an obscure research contest. It was 2012, the third year of the annual ImageNet competition, which challenged teams to build computer vision systems that would recognize 1,000 objects, from animals to landscapes to people. In the first two years, the best teams had failed to reach even 75% accuracy. But in the third, a band of three researchers--a professor and his students--suddenly blew past this ceiling. They won the competition by a staggering 10.8 percentage points.
Define some objective, then let a population of algorithms compete against one another, mixing and refining characteristics of the most successful of their predecessors, and occasionally introducing novel approaches. They are rather ubiquitous in today's research and commercial environment, but what are they good for, and what should one know about them? I'm here to ask some of these questions, and maybe produce a few answers along the way. Although it can be misleading to mix metaphors in a discipline that already overburdens its terminology over different granularities and levels of description, I invoked the essence of genetic algorithms in order to convince people that an observable of any recurring competition is that the metric it judges contestants by tends to be optimized over time. I also wanted to get the idea of diversity and its relationship with an incentivized selection process such as competition primed in our minds.