congerous

> truism

Maybe, be people in this thread treat Tensorflow's creation as an act of simple altruism.

> And if "anywhere" includes the users' hardware, it's wrong: tensorflow runs flawlessly on any Linux/NVIDIA hardware. Maybe it works better with GCE than AWS, but that would once again fall into that "rather unsurprising" category of factoids.

Sorry, Tensorflow is slow on GPUs compared to other frameworks. This is not just an early blip, its a consistent pattern that has been repeatedly demonstrated. Why is Tensorflow slow on commodity hardware? Why isn't Google with it's infinite resources making Tensorflow run as fast as other frameworks on GPUs? Because it needs to demonstrate an advantage on the Google Cloud with TPUs.

On that cloud, it surrounds Tensorflow with other functionality that makes it easy to build AI, which aren't part of the Tensorflow project. Tensorflow is hard and inefficient to serve for inference, for example.

Machine learning is Google cloud's only hope to salvage Diane Greene's efforts and extend their dominance to a new sector. They're running a distant fourth.

> actually doesn't sound that scary.

It sounds scary to a lot of companies that don't want to be controlled or destroyed by Google. But by all means, lend them a hand, geek soldier.

No. The US has the lead because its competitive advantage in the world economy is exporting innovation. That's what it's good at. The Chinese economy is outperforming the American one in many ways, but the US enjoys a more open society and economy, which encourage innovation. No one would ever say that the West should be complacent, but people are making a big deal out of the Chinese AI push, exaggerating it in a way, and that aligns with more widespread fear-mongering about China. In reality, corruption, chaos and censorship continue to hold China back more than is being reported.

I'm surprised I have to write this, but Google is not a charity. They are pouring commercial resources into Tensorflow for a reason. That reason is Google Cloud. Tensorflow is a Trojan horse to get people to use Google Cloud and other paid Google products. How do I know this? Because Tensorflow works better on Google Cloud than anywhere else, and Google is making a concerted effort to catch up with AWS in cloud, mostly through machine learning.

I didn't compare Tensorflow to Android services. I said that Tensorflow would serve as the basis of a service bundle, much like Android did. Let's come back in a couple years and I'll tell you I told you so.

TensorFlow: everything to all people.

Eager is actually not as innocent as "open-source projects borrowing the best parts from each other", as some commenters here suggest.

Google is attempting to dominate the machine-learning API and the Python ecosystem for scientific computing.

The company that controls the API influences which apps are built on it and how. Think about how Google bundled Android services on top of Android, and how that posed an existential threat to other companies. That's what's coming for TensorFlow. Many developers are too naive to realize it, or too short-sighted to care.

This article adds nothing new to the discussion about AI.

* We knew about Tencent's AI Lab (Saying you need your own capabilities is another way of saying: We have NIH and we will waste 3 years building our own framework from the bottom up).

* We knew about China's AI plan, which was announced in July. That was huge news at the time.

* We knew about Baidu, but Baidu is actually slipping. They lost Andrew Ng and Adam Coates, and frankly they have executed poorly on social media and cloud computing compared to Tencent and Ali. It looks like they'll lose their lead in AI.

And the article actually contradicts its own sub-headline: "The West shouldn’t fear China’s artificial-intelligence revolution. It should copy it." The West is leading in AI research, and China is copying the West. So what does it mean that the West should copy China? China is playing catch up in AI just like it has been playing catch up in GDP growth. Catching up always moves faster than blazing a trail. The West is blazing a trail now. They don't have much to learn from players trying to catch up.

Data scientists arguably have too much choice. 10 data scientists will have 50 different tools, can't share work or build on another's experiments or even remember what the result of an experiment were. those are some of the reasons why most data science projects fail. that and integrations. standardization has real benefits.

Curious: Does anyone use GANs for anything of value, or are they just gimmick NNs?

KPI = amount of funds raised.

I've been there. A ton of good advice is on this thread. You need to try to influence your internal state chemically if you want to stay on track. Exercise, medication, meditation, eating and sleeping healthy are all helpful. It's also good to reconnect with people that matter to you: family, friends, etc. If you're closing the deal, and the money is significant, just think what you could do for them, or for other people who need help. And find ways to spend more time with them, because our lives are defined by our relationships. You could say your self exists to the extent that your in conversation with people who understand you. So go find with and be with them. It'll help you refuel.

TF is way behind on UI, which is why it's making Keras its front-end. It's fairly slow on multi-GPUs compared to Torch and neon. It might pull ahead in performance on GCE, but that's just for lockin.

The "leading" DL framework is playing catch-up to Chainer, PyTorch and DyNet. Another Google product development bungle.

I have to say I find this piece pretty disappointing. Slightly smug throughout, which is easy in retrospect, with approximately zero to add to the conversation. The thesis seems to be: The future is hard to predict, and even if you can, it's hard to get the timeline right. No shit. Is this what VCs do with their spare time? Remember granddad and contemplate uncertainty?

The assumptions we make, the more performance goes down in tasks like machine translation, and that includes the assumption that parts of speech matter to machines.

> Do they care less than they used to, though?

That's impossible to measure, but the arguments you have employed regarding personnel changes do not demonstrate your point.

There's some baseless speculation in this thread about what's Singhal's departure meant for Google search. It's important to remember that Singhal was committed Google's knowledge graph, a painstaking, manually constructed graph of entities and relationships that surfaces the occasional answer (ask Google what the capital of Azerbaijan is and you will see a knowledge graph answer). Knowledge graphs are brittle, hard to extend, and hard to modify in the face of changing data. Google has tilted towards machine learning and neural nets for a lot of functions, and Singhal was not part of that tilt. But you could argue that machine learning and neural nets are producing better results, which counters the argument that Singhal leaving means that Google doesn't care about organic results any more. That's BS, frankly.