In spring, we went to North America, but now in the autumn we get to stay around in our own part of the world. GTC Europe is a little sister of the main conference in Silicon Valley, but still very interesting and inspiring and therefore definitely worth a visit. It touches more than one of Berge's interests with its GPU oriented collection of speakers, exhibitors, and visitors. Jakob Andersson and Peter Karlsson made sure to spend the two days in Amsterdam absorbing all the news and trends.
Nvidia and the keynote
Enter Jen-Hsun Huang in his characteristic leather jacket. Follow two hours of CEO characteristic, product launching, super–hyper dripping, deep learning loving, and GPU hyping keynote!
GTC is Nvidia's conference and it shows, but it also adds a lot of fun with their full commitment to show off their products. The keynote mixed some product launches and partnership announcements with both impressive and some not so impressive deep learning demos. What is the value in predicting potential viewers to shared live video in real time? And what should Rembrandtising a video on the fly make us think of? Well, the future might tell.
The big surprise for us was the announced partnership with SAP to bring deep learning to enterprises around the world through Nvidia hardware and the SAP products. In hindsight, it seems a logical step due to all the data in their systems and all the benefits deep learning could bring to SAP's customers, but it surprised us a bit since we do not associate them with innovation, at least not in Sweden. However, it is never too late to change and we want to be the first to applaud trying out new things.
Automotive is a big focus of Nvidia today. They launched an extended Drive PX 2 range with smaller solutions for companies not targeting full automation. Xavier – their next-gen SOC introduced at the end of the keynote – will take further steps to adapt their products for autonomous drive by bringing down power consumption and raising systems safety capabilities (ASIL C claimed). They launched a partnership with TomTom on HD mapping, in the end also targeting autonomous vehicles. To show off the capabilities, Jen-Hsun talked at length about their own autonomous vehicle (more on that below) and their software platform called DriveWorks.
Everything else was of course not forgotten! VR got some attention, especially at the show where several demos – using the same HTC Vive we use at the Berge office – where setup to show what is happening in gaming and in industry. High-performance computing was the fourth area of focus at GTC this time. They showed the new Tesla P4 and P40 accelerators and their partnership with IBM to bring GPUs to data centres. Both VR and HPC had their own tracks at the conference, but focusing on deep learning during this visit, we were unable to visit most of them.
In the end, it is clear that Nvidia is committed to bringing the computational performance needed by deep learning, autonomous drive, and other advancements. And, of course, they should be! They have been given a massive opportunity with the new demands that their existing product, the GPU, is so well suited to solve. However, we are glad someone brings that computational power; that this year's ImageNet winner is four times deeper than Microsoft's ResNet that won 2015 show that we are going to need that power!
Deep learning and Artificial Intelligence
Machine learning and artificial intelligence are growing fast through the deep learning revolution. Berge believes we will see disruption of many industries in the near future. Most visitors to GTC seem to agree (of course, selection bias!).
During the keynote, Nvidia showed statistics indicating that the number of deep learning developers has grown by a factor of 25 in the last two years. They did not show any source, but it is apparent that something big is happening.
On the fun side, it was great to listen to one of the big names in DL, Google DeepMind, and how they set out on a mission to first "solve intelligence" to then "solve everything else". Dominik Grewe walked us from how to solve Atari games to AlphaGo and its network architecture. Although not that applicable, games are a really good platform for trying out new things since you have full control and since they are designed to be challenging for humans. It will be interesting to follow them in the coming years!
Two talks that were more applicable came from Russian Yandex and a cooperation between DFKI and PwC. Yandex showed a deep learning solution to upload information about free parking spots to their map service by extracting that information from video data – either from dash cams common in Russia or from cell phones used for their navigation service. It seemed to work well so expect that to happen soon. As a fun side-story to show the power of their network these guys had mounted a camera on a bike when arriving in Amsterdam (city of bikes, right!) and after running that data through their algorithms it actually worked well even though the video was shaky, shot in a different angle, and from a new environment. DFKI, a German AI research centre, and PwC showed how to use deep learning to find potential fraudulent transaction in company-internal data. Their solution could flag transactions for further inspection by an auditor by finding outliers across the full data dimensions (compared to traditional approaches by looking at outliers in individual dimensions and thus finding mostly false-positives). This is really a field with commercial and societal interest, but it is hampered by two difficulties: (1) fraudsters are trying to hide their transactions and (2) the data is confidential so there exists no public dataset to experiment on or to compare algorithms on, c.f. ImageNet in object recognition. However, the results show that these difficulties can be surmounted even though it may take longer time.
On the practical side of deep learning, there was some Swedish participation through Martin Englund from the Facebook DevOps team. Martin talked about how Facebook have automated their research data centre to the extent that it identifies broken GPUs and schedule maintenance. On the scale Facebook operates, GPUs fail regularly so automating operations helps the deep learning engineers focus on developing new things. Impressive.
Marek Rosa made one of the most memorable appearances. He had always dreamt about solving general artificial intelligence so he started his own research company called GoodAI and financed it himself with money he had made developing games – just because he "did not have time for investors". As if that is a choice we all can make!
Autonomous driving seems to be on everyone’s agenda today and GTC Europe proved no different. Besides academic presence, Volvo Cars, Audi, and Renault represented the automotive industry and Nvidia represented the tech industry. It more and more seems like two sides in the race even if it is not as straightforward as tech vs. auto.
The classical engineering approach builds on the achievements of the last decade, such as adaptive cruise control and lane support functions, and adds additional sensors and redundant electronic architectures to widen the scope and diminishing failure rates. According to their talks, both Volvo and Renault favour this approach.
On the other side of the spectrum, we find end-2-end deep learning where raw sensor data is fed into a neural network tasked with outputting control commands. This is what Nvidia is doing with their research vehicle BB-8, which can be seen in the video to the right. BB-8 has learned the basic behaviour of staying in lane or on the road from seeing how humans drive. The net trained in BB-8 – called PilotNet – is used as a base behaviour along with other modules in Nvidia's autonomous drive OS DriveWorks.
In-between these extremes we see the modular approach where the problem is divided into parts that can be solved by either deep learning or traditional handcrafted algorithms. Audi presented their work on evaluating both this and end-2-end deep learning, and they provided positive reviews on the use of synthetic data for training neural networks on real world problems. That is very encouraging for our own strategies that aim in the very same direction.
GTC Europe did not shed any new insights on which side will be first to put autonomous vehicles (level 4) on the market for us consumers to enjoy. It is, however, clear that the different sides focus on slightly different questions, in line with their own strengths (or the focus is what creates those strengths). Volvo and Renault both stress the safety and liability aspects while Nvidia focused on what their car is able to do. In the end, one needs to provide both a capable AI driver and a safe electrical solution and at Berge we are sure the tech side have some tough nuts to crack (which we, of course, can help with) before being commercially viable, but we believe that the classical approach is a dead end with no hope of scaling to city traffic or to the rough traffic of other parts of the world. Sorry.
Embedded and virtual reality
At the show, several companies displayed their solutions for embedding GPUs into products from drones and robots to military vehicles and space applications, many of them using the Jetson TX1 platform. Bringing GPUs from the consumer market to tough environments in military and space will require improvements in robustness and power efficiency that hopefully will benefit consumers as well, but that definitely will aid Nvidia's incursion into automotive.
That VR is making its way into business is clear. IKEA hosted an informative and funny talk about how they are using the technology today. Maybe you had missed it, but they actually released a VR kitchen simulator on Steam earlier this year. It is quite impressive and gives a much better feeling than visiting a store or browsing their web. A fun note is that they had as many downloads when launching an update adding meatballs as on launch! If you do not have HTC Vive, you can always come by our office and try out the simulator (otherwise as well, by the way).
IKEA use the Unreal Engine, which we also use in our simulators (e.g. this), and using game engines was actually a small theme across the conference. Several academics discussed virtual proving for automotive applications. Yandex said they use virtual data to enhance their network training. Google DeepMind is moving into teaching their AI to play 3D games. At Berge, we have discussed this for a time since it connects the strengths of our visualisation team with the strengths of our deep learning team. It is good to see others thinking along the same lines.
Link to the event: http://www.gputechconf.eu