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.

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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?

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Trudel added, “We’re starting to see AI having a significant impact on cloud computing. If you extrapolate what the analysts are saying, there’s faster growth in AI, with 10% of cloud revenue expected to come from AI by 2025.”

Trudel noted that although major cloud suppliers are already offering AI capabilities, cloud-based AI services market is still at a nascent stage. He said AWS’s approach to AI is interesting because it uses AI to help organisations easily determine the best open-source machine learning frameworks, such as TensorFlow or MXNet to use for crunching different types of data. AWS also offers Common Crawl, a publicly available dataset that contains petabytes of data collected over years of web crawling, Trudel said.

Trudel noted that Google has been doing a lot of work in AI, whether it is “helping developers choose the best algorithm for AI projects, using its Deepmind technology to build AI services or open-sourcing TensorFlow and Android to gain mindshare in AI developments”. Additionally, Google is also using AI to improve the energy efficiency of its datacentres. “Now, national grid providers are also looking at leveraging the same [AI] models to drive efficiency in their networks,” he said.

Trudel said that major cloud suppliers are working towards delivering general AI services. He is also expecting companies such as Apple, Baidu, Alibaba, Intel, Tencent and Facebook to join the fray with their own AI services.

Do you agree with Trudel’s comments?

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