While still technically in pre-release, the Mac-optimised TensorFlow fork supports native hardware acceleration on Mac devices with M1 or Intel chips through Apple’s ML Compute framework.

The new TensorFlow release boasts of an over 10x speed improvement for common training tasks. While impressive, it has to be taken in the context that the GPU was not previously used for training tasks. 

A look at the benchmarks still indicates a substantial gap between the Intel and M1-based Macs across various machine learning models:

In a blog post, Pankaj Kanwar, Tensor Processing Units Technical Program Manager at Google, and Fred Alcober, TensorFlow Product Marketing Lead at Google, wrote:

“These improvements, combined with the ability of Apple developers being able to execute TensorFlow on iOS through TensorFlow Lite, continue to showcase TensorFlow’s breadth and depth in supporting high-performance ML execution on Apple hardware.”

We can only hope that running these workloads doesn’t turn MacBooks into expensive frying pans—but the remarkable efficiency they’ve displayed so far gives little cause for concern.

The post TensorFlow is now available for those shiny new ARM-based Macs appeared first on AI News.

The Huawei Ascend 910 is the “world’s most powerful AI processor,” according to a press release on Friday. The chip’s specs were first announced during last year’s Huawei Connect event in Shanghai.

Eric Xu, Rotating Chairman of Huawei, said:

“We have been making steady progress since we announced our AI strategy in October last year. Everything is moving forward according to plan, from R&D to product launch.

We promised a full-stack, all-scenario AI portfolio and today we delivered, with the release of Ascend 910 and MindSpore. This also marks a new stage in Huawei’s AI strategy.”

Huawei claims the final version of the Ascend 910 not only performs as promised, but it does so with much lower power consumption.

For half-precision floating point (FP16) operations, Ascend 910 delivers 256 TeraFLOPS performance. For integer precision calculations (INT8), it delivers 512 TeraOPS.

Huawei initially expected the Ascend 910’s max power consumption to be 350W but the company has managed to deliver the promised performance with a max consumption of just 310W.

“Ascend 910 performs much better than we expected,” said Xu. “Without a doubt, it has more computing power than any other AI processor in the world.”

Alongside the Ascend 910, Huawei has launched an AI computing framework called MindSpore.

Last year, Huawei announced three goals for MindSpore:

• Easy development: Reduce training time and costs.
• Efficient execution: Use the least amount of resources with the highest possible OPS/W.
• Adaptable to all scenarios: Including device, edge, and cloud applications.

Huawei claims that MindSpore requires 20 percent fewer lines of code than other leading frameworks when used for a typical neural network for natural language processing.

“MindSpore will go open source in the first quarter of 2020,” said Xu. “We want to drive broader AI adoption and help developers do what they do best.”

The Chinese tech behemoth continues to expand its presence despite battling a US trade ban. The US has been pressuring its allies to ban Huawei over concerns it poses a national security threat.

While security must always be prioritised, few can dispute the innovation which Huawei brings across its business. Today’s announcements show the kind of innovations which US companies may miss out on if a deal cannot be reached, putting them at a disadvantage to Chinese rivals.

Interested in hearing industry leaders discuss subjects like this? , , , AI &

The post Huawei unveils high-end AI chip for servers alongside MindSpore framework appeared first on AI News.

Reinforcement learning has been used for some of the most impressive AI demonstrations thus far, including those which beat human professional gamers at Alpha Go and Dota 2. Google subsidiary DeepMind uses it for its Deep Q-Network (DQN).

Building a reinforcement learning framework takes both time and significant resources. For AI to reach its full potential, it needs to become more accessible.

Starting today, Google is making an open source reinforcement framework based on TensorFlow – its machine learning library – available on GitHub.

Pablo Samuel Castro and Marc G. Bellemare, Google Brain researchers, wrote in a blog post:

“Inspired by one of the main components in reward-motivated behavior in the brain and reflecting the strong historical connection between neuroscience and reinforcement learning research, this platform aims to enable the kind of speculative research that can drive radical discoveries.

This release also includes a set of collabs that clarify how to use our framework.”

Google’s framework was designed with three focuses: flexibility, stability, and reproducibility.

The company is providing 15 code examples for the Arcade Learning Environment — a platform which uses video games to evaluate the performance of AI technology — along with four distinct machine learning models: C51, the aforementioned DQN, Implicit Quantile Network, and the Rainbow agent.

Reinforcement learning is among the most effective methods of training. If you’re training a dog, offering treats as a reward for the desired behaviour is a key example of positive reinforcement in practice.

Training a machine is a similar concept, only the rewards are delivered or withheld as ones and zeros instead of tasty goods or a paycheck.

“Our hope is that our framework’s flexibility and ease-of-use will empower researchers to try out new ideas, both incremental and radical,” wrote Bellemare and Castro. “We are already actively using it for our research and finding it is giving us the flexibility to iterate quickly over many ideas.”

“We’re excited to see what the larger community can make of it.”

What are your thoughts on Google’s open-sourcing of its reinforcement learning framework?

The post Google improves AI model training by open-sourcing framework appeared first on AI News.