Tesla may be introducing machine-learning training as a web service with its upcoming'Dojo' supercomputer, CEO Elon Musk said on Twitter. Project Dojo was initially revealed by Musk last year and is a supercomputer which Tesla has been working on. The supercomputer has been designed to ingest massive amounts of video data and perform massive levels of unsupervised training on the visual data. The goal of Dojo will be to be able to take in vast amounts of data and train at a video level and do massive unsupervised training of vast amounts of video data. Dojo uses our own chips & a computer architecture optimized for neural net training, not a GPU cluster. Could be wrong, but I think it will be best in world.
It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".
The development of autonomous vehicles has been the strongest driver of auto tech investment in the past couple of years. According to the infographic about the future of cars from carsurance.net, more than $9 billion was funneled into the R&D of self-driving vehicles between 2014 and 2018 in 215 deals. The collective efforts of traditional automakers and tech giants, such as Google, Amazon, and Apple, are fast-tracking the maturity of autonomous driving technology. By 2030, about 70% of motor vehicles are projected to have some self-driving features. Furthermore, by the year 2035, it is expected that there will be 4.5 million self-driving cars roaming around the US streets.
Artificial Intelligence (AI) is rapidly changing the world. Emerging technologies on a daily basis in AI capabilities have led to a number of innovations including autonomous vehicles, self-driving flights, robotics, etc. Some of the AI technologies feature predictions on future and accurate decision-making. AI is the best friend to technology leaders who want to make the world a better place with unfolding inventions. Whether humans agree or not, AI developments are slowly impacting all aspects of the society including the economy.
The deep learning component of AI can be a high-performance computing problem as it requires a large amount of computation and a data motion (IO and network). Deep learning needs computationally-intensive training and lots of computational power help to enable speeding up the training cycles. High-performance computing (HPC) allows businesses to scale computationally to build deep learning algorithms that can take advantage of high volumes of data. With more data comes the need for larger amounts of computing needs with great performance specs. This is leading to HPC and AI converging, unleashing a new era.
What do automotive shoppers really want? A few years ago, those who kicked the tires on new vehicles might have prioritized fuel efficiency, comfort, or perhaps horsepower. "The race never ends to develop'must have' vehicle technologies," says Kristin Kolodge, executive director of driver interaction and human machine interface research at J.D. Power. "New technology continues to be a primary factor in the vehicle purchase decision." "However, it's critical for automakers to offer features that owners find intuitive and reliable," Kolodge adds.
Because this year's UseR 2020 in Munich couldn't happen as an in-person event, I will be giving my workshop on Deep Learning with Keras and TensorFlow as an online event on You can register for FREE via Eventbrite. Deep learning is an artificial intelligence that mimics the workings of a human brain in processing different data, creating patterns and interpreting information that is used for decision making. It is a subfield of machine learning in artificial intelligence and Its networks has the capability to learn, supervised or unsupervised, from data that is either structured or labelled. It is one of the hottest trends in machine learning at the moment and there are many problems where deep learning shines, such as Self Driving Cars, Natural Language Processing, Machine Translations, image recognition and Artificial Intelligence (AI) and so on.
Sometimes you initiate an action and in a domino-like manner it gets going and going, seemingly feeding off itself and rapidly agitating in an almost unstoppable manner. For example, you might be familiar with those popular YouTube videos of a beaker that when filled with a special liquid will spontaneously gush out foam, akin to a type of chain reaction. History indicates that during the initial creation of the atomic bomb, some of the scientists involved were concerned that if the atomic bomb was set off, it might begin a chain reaction due to igniting a fission explosion in the air, and would generate a globally wide conflagration. There is a venue today in which a chain reaction phenomenon is being bandied about by researchers and scientists. Some vehemently assert that we are potentially going to have an AI "intelligence explosion" that will someday occur, and there are various bets that this might happen somewhere between the year 2050 and the year 2100.
To build a machine learning model dataset is one of the main parts. Before we start with any algorithm we need to have a proper understanding of the data. These machine learning datasets are basically used for research purposes. Most of the datasets are homogeneous in nature. We use a dataset to train and evaluate our model and it plays a very vital role in the whole process. If our dataset is structured, less noisy, and properly cleaned then our model will give good accuracy on the evaluation time. Imagenet dataset is made by the group of researchers and the images in the dataset organized according to the WordNet hierarchy. This dataset can be used for machine learning purposes and computer vision research fields as well.
Safety is the central focus on driverless vehicle systems development. Artificial intelligence (AI) is coming at us fast. It's being used in the apps and services we plug into daily without us really noticing, whether it's a personalized ad on Facebook, or Google recommending how you sign off your email. If these applications fail, it may result in some irritation to the user in the worst case. But we are increasingly entrusting AI and machine learning to safety-critical applications, where system failure results in a lot more than a slight UX issue.