In the News
For biologists, identifying the precise shape of a protein often requires months, years or even decades of experimentation.
Now, an artificial intelligence lab in London has built a computer system that can do the job in a few hours — perhaps even a few minutes.
On 9-10 December, Xilinx will explore key AI and software development trends, and will discuss how to accelerate any application with custom, adaptive hardware. Aimed at AI and software developers, you’ll leave knowing how Xilinx platforms can be used to accelerate any application and evolve as requirements change.
In The News
Loon Huge stratospheric balloons that act as floating cell towers in remote areas can stay in the air for hundreds of days thanks to an artificially intelligent pilot created by Google and Loon.
Security researchers had devised a way to attack a Proofpoint product that uses machine learning to identify spam emails.
Applied use cases
Making a successful machine learning project is an incremental process.
In fact, chances are that you will probably spend more time working on the infrastructure of your system, than on the machine learning model itself: Complete infrastructure should be independent of the machine learning model.
Launched by Amazon’s cloud arm AWS, the new machine-learning-based services include hardware to monitor the health of heavy machinery and computer vision capable of detecting whether workers are complying with social distancing.
“Automation gives people the bandwidth and breathing room to do more interesting, more inspiring, and more valuable work that moves the business forward such as building customer relationships or making hard decisions on what to do next.”
Many organizations are seeking to remain operational in the short term by automating tasks that would otherwise be carried out by humans.
China is currently the largest and fastest-growing market in the world for industrial robotics, with a 21 percent increase up to $5.4 billion in 2019.
Choosing the right shape will be vital for your robot’s ability to traverse a particular terrain. And it’s impossible to build and test every potential form. But now an MIT-developed system makes it possible to simulate them and determine which design works best.
School of Engineering, The University of Tokyo, Tokyo, Japan DeepX Inc., Tokyo, Japan To endow robots with the flexibility to perform a wide range of tasks in diverse and complex environments, learning their controller from experience data is a promising approach.
Human gender biases that limit recruitment opportunities for women are mimicked and exacerbated by artificial intelligence (AI) used for sorting resumés, according to new research.
Microsoft and Code.org are excited to announce a partnership that gives every student from elementary school to high school the opportunity to learn about artificial intelligence (AI).
The development of Artificial Intelligence (AI) is an important factor in this epochal revolution.
This is why initiatives such as the OECD Principles on Artificial Intelligence and, ultimately, good international regulations are essential to ensure AI remains a powerful force for good.
In these two works, with fellow Microsoft Research New England researchers Greg Lewis and Lester Mackey along with MIT student Nishanth Dikkala, we propose a novel way of estimating flexible causal models with machine learning from non-experimental data, blending ideas from instrumental variable…
QML Background QML has important similarities and differences to traditional neural network/deep learning approaches to machine learning.
An important type of QML that TFQ provides techniques for is called variational quantum circuits (QVC).
The deep learning framework PyTorch has infiltrated the enterprise thanks to its relative ease of use. Three companies tell us why they chose PyTorch over Google’s renowned TensorFlow framework.