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Radical AI podcast: featuring Veena Dubal

AIHub

Hosted by Dylan Doyle-Burke and Jessie J Smith, Radical AI is a podcast featuring the voices of the future in the field of artificial intelligence ethics. In this episode Jess and Dylan chat to Veena Dubal about the ethical crisis of the gig economy. What is precarious work and how does it impact the psychology of labor? How might platforms like Uber and Lyft be negatively impacting their workers? How do gig economy apps control the lives of those who use them for work?


Is Tesla Entering Machine Learning As A Service Market?

#artificialintelligence

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.


At CAGR 36.2%, Artificial Intelligence Market 2020: Future Challenges And Industry Growth Outlook 2025

#artificialintelligence

Artificial Intelligence (AI) is the study of "intelligent agents" which can be define as any device that perceives its environment and takes appropriate action that makes the highest probability of achieving its goals. Additionally, it can also be define as a system's ability to interpret external data, learn from gathered data and use those learnings to realize specific goals through adaptation. It is also called as machine intelligence and attributed to the nature of intelligence demonstrated by machines. Some of the features of artificial intelligence are; successfully understanding human language, contending at the highest level in strategic games systems such as chess and go, autonomously operating cars, intelligent routing in content delivery networks and military simulations and others. To solve the problem of learning and perceiving the immediate environment, many approaches have been taken such as statistical methods, computational intelligence, versions of search and mathematical optimization, artificial neural networks, and methods based on statistic, probability and economics.


Vision Processing Units (VPUs)

#artificialintelligence

On my first day working for MILLA, an autonomous shuttle company, I discovered a shuttle that can drive up to 30 km/h; quite an improvement if you compare it to our competitors at the time driving at 5–8 km/h. At the time, the shuttle was new and there was no GPU yet on it. In case you don't know what a GPU is, here's a quick picture that explains it well: A GPU (Graphic Processing Unit) parallels the processes so operations are done faster. In a self-driving car, this can be super useful for computer vision or point cloud processing. It was first released in video games because of the need to display multiple things at the same time.


The Future of AI Part 1

#artificialintelligence

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".


4 Perceived Problems With Self-Driving Vehicles

#artificialintelligence

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.


Computer Vision for Pictures and Videos

#artificialintelligence

As living organisms process images with their visual cortex, many researchers have taken the architecture of the mammalian visual cortex as a model for neural networks structured to perform image recognition. Over the past 20 years, progress in computer vision has been remarkable. Some computer vision systems achieve 99% accuracy, and some run decently on mobile devices. Today's best image classification models can detect diverse catalogues of objects at high definition resolution in colour. Additionally, people sometimes use hybrid vision models that combine deep learning with classical machine-learning algorithms and perform specific sub-tasks.


Redefining Leadership in the Age of Artificial Intelligence

#artificialintelligence

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.


Why High Performance Computing Could Become The Next Frontier For Enterprise AI

#artificialintelligence

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.


Buying a new car? These tech features could drive your choice

USATODAY - Tech Top Stories

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.