Abstracting ML code sacrifices expressiveness, increases coupling, and aggravates maintenance. These might be ok for regular software. But things are different for ML. I am sure you know how it feels to waste hours trying to match the API when you want to implement an ML trick. APIs and abstractions are bad for fast-paced ML R&D. ML is too fast, and any API is outdated from its inception. We see a similar pattern with well-known ML libraries (Transition from Theano - Tensorflow - PyTorch - JAX…).

Can we get a fanfare of trumpets, please? The time has come to introduce you to our TC Top Picks for Disrupt Berlin 2019. The ingenuity and creativity reflected in the international startup community can't be overstated, and narrowing the field from the hundreds of applications we received was no easy task. Challenging as it was, TechCrunch editors had a (wicked fun) job to do -- select up to five early-stage startups they felt represent the best of their specific tech category. This remarkable cadre of early-stage startups knocked our proverbial socks off.

Who's ready to save some money on passes to Disrupt Berlin 2019, our premier tech conference that takes place on 11-12 December? Then listen up, because our super early bird pricing ends in just five days. Right now, passes start at €345 VAT and, depending on which pass you choose, you can save up to €600. Buy your passes to Disrupt Berlin before the Friday, 11 October at 11:59 p.m. (CEST) deadline. Then plan your strategy to make sure you take full advantage of Disrupt.

If you're the founder of an early-stage startup here's a big, fat reminder about one of the great experiences you can have at Disrupt Berlin 2019 on 11-12 December. Apply to our TC Top Picks program for a chance to step into the startup spotlight. If you're selected, you'll receive a free Startup Alley Exhibitor Package and a bunch of other perks (more on those in a minute). TechCrunch editors will accept applications from early-stage startups that fall into one of the following tech categories: AI/Machine Learning, Biotech/Healthtech, Blockchain, Fintech, Mobility, Privacy/Security, Retail/E-commerce, Robotics/IoT/Hardware, CRM/Enterprise and Education. They'll thoroughly review every application and then select up to five startups they feel represent the very best in each category.

Toyota announced creation of a $100 million venture fund to invest in autonomous driving and robotic technology start-ups as automakers increasingly push into the self-driving market. Toyota AI Ventures, a Silicon Valley-based subsidiary of Toyota, plans to invest the said amount into early-stage startups that are developing "disruptive" technologies in those fields, the company said. Jim Adler, managing director of Toyota AI Ventures, said in a statement, "Auto manufacturers must participate in the startup ecosystem to stay ahead of the rapid shift in the auto industry." The company added that the fund is part of Toyota's mission is a futuristic "discover what's next" phase. Toyota's AI venture fund has already invested in 19 different start-ups over the last two years, bringing its total funding commitment to autonomous driving technology to $200 million, the company further stated. Toyota AI Ventures looks for early-stage startups, across a range of industries, that are applying AI, data, and cloud technologies to tackle important problems and create new market opportunities.

Silicon Valley-based venture capital firm Toyota AI Ventures today announced Fund II, a new $100 million fund dedicated to investing in early-stage startups developing disruptive technologies and business models in the autonomous mobility and robotics markets. Led by founding Managing Director Jim Adler, who also serves as executive advisor at the Toyota Research Institute (TRI), Toyota AI Ventures (TAIV) was founded in July 2017 as a subsidiary of TRI. In less than two years, Toyota AI Ventures has invested in a diverse portfolio of 19 startups. Fund II brings the firm's total assets under management to more than $200 million to support startups around the world. "Auto manufacturers must participate in the startup ecosystem to stay ahead of the rapid shift in the auto industry," stated Adler.

If you have a job today, there's a good chance you personally reached out to your employer and interviewed with other humans to get it. Now that you've been there a while, it's also likely the workday feels more like a long slog than the fulfilling career move you had envisioned. But if today's early-stage startups have their way, your next employment experience could be quite different. First, forget the networking and interview gauntlet. Instead, let an AI-enabled screening program reach out about a job you don't seem obviously qualified to do.

Today, Google is launching a new investment program for early-stage startups working to broaden Google Assistant hardware or features. The new program provides financial resources, early access to Google features and tools, access to the Google Cloud Platform, and promotional support in efforts to bolster young companies. Google says its investment program will also support startups focussing on Google Assistant's use in travel, hospitality, or games industries. The startups will receive advice from Google engineers, product managers, and design experts who will guide the young companies through technical and product development. Google notes it's looking at creating new devices and ideas that use Google Assistant in fresh ways.

Crunchbase News has covered the U.S. FinTech space a great deal since the start of the quarter. From looking at FinTech's early-stage stars of 2017 and 2018 to NYC's growing fintech scene, to traveling down south to Atlanta, which has a FinTech startup scene of its own. To get a hold on the space in Q1 2018, we take a look at venture dollar invested into U.S.-based FinTech startups quarter by quarter since 2016. Crunchbase News's definition of FinTech encompasses a broad range of startups that leverage deep learning technology and big data in order to streamline tax processes, make paying friends easier, and provide better insurance options.1 Since 2016, more than $15.6 billion has been invested in seed, early, and late-stage U.S.-based FinTech startups. Total dollar volume saw an increase of 25 percent year over year from 2016 to 2017.

When considering a startup, especially an early-stage startup, investors want to conduct as much due diligence as possible. What little data they can gather is scattered all over different sources including Crunchbase, LinkedIn, Pitchbooks, company websites, etc. Consolidating this data takes a great amount of time and effort. Furthermore, the data sets can be incomplete or biased depending on the search queries -- imagine overlooking a keyword. To make the due diligence process fairer and less cumbersome for investors, various platforms are using machine learning (ML) to pull together information about startups from all available resources to help investors assess companies and investment opportunities. But where machine learning really shines is in the interplay of data-driven insights that are qualified by human intuition and personal experience.