Deepfakes have gained a lot of negative attention recently. Be it the hugely criticized DeepNude AI app which removes clothing from pictures of women or the FakeApp that swaps the faces of celebrities with porn stars in videos. Deep-learning algorithms are excellent at detecting matching patterns in images. This capability can be used to train neural nets to detect different types of cancer in a CT scan, identify diseases in MRIs, and spot abnormalities in an x-ray. While the idea of implementing deepfake AI for medical purposes sounds great, researchers don't have enough data to train a model -- simply because of privacy concerns.
The hardware is already being used to improve the performance of things like prosthetic limbs. The news: Intel has just unveiled Pohoiki Beach, a system that contains 64 of its Loihi AI processors. These are so-called neuromorphic chips that seek to imitate the learning ability and energy efficiency of human brains. Although the technology is still in its infancy, it's proving popular with researchers training various kinds of AI applications. A silicon leg up: Pohoiki Beach can perform certain data-crunching tasks up to 1,000 times faster than more general-purpose processors such as CPUs and GPUs, while using much less power.
Significant advances are being made in artificial intelligence, but accessing and taking advantage of the Machine Learning systems making these developments possible can be challenging, especially for those with limited resources. These systems tend to be highly centralized, their predictions are often sold on a per-query basis, and the datasets required to train them are generally proprietary and expensive to create on their own. Additionally, published models run the risk of becoming outdated if new data isn't regularly provided to retrain them. We envision a slightly different paradigm, one in which people will be able to easily and cost-effectively run Machine Learning models with technology they already have, such as browsers and apps on their phones and other devices. Through this new framework, participants can collaboratively and continually train and maintain models, as well as build datasets, on public blockchains, where models are generally free to use for evaluating predictions.
By Ariel Procaccia Last March, McDonald's Corp. acquired the startup Dynamic Yield for $300 million, in the hope of employing machine learning to personalize customer experience. In the age of artificial intelligence, this was a no-brainer for McDonald's, since Dynamic Yield is widely recognized for its AI-powered technology and recently even landed a spot in a prestigious list of top AI startups. Neural McNetworks are upon us. Trouble is, Dynamic Yield's platform has nothing to do with AI, according to an article posted on Medium last month by the company's former head of content, Mike Mallazzo. It was a heartfelt takedown of phony AI, which was itself taken down by the author but remains engraved in the collective memory of the internet.
Eta Compute has developed a high-efficiency ASIC and new artificial intelligence (AI) software based on neural networks to solve the problems of edge and mobile devices without the use of cloud resources. Future mobile devices, which are constantly active in the IoT ecosystem, require a disruptive solution that offers processing power to enable machine intelligence with low power consumption for applications such as speech recognition and imaging. These are the types of applications for which Eta Compute designed its ECM3531. The IC is based on the ARM Cortex-M3 and NXP Coolflux DSP processors. It uses a tightly integrated DSP processor and a microcontroller architecture for a significant reduction in power for the intelligence of embedded machines.
To simplify the path toward enterprise AI, organizations are turning to IBM Watson Studio and Watson Machine Learning. Together with IBM Watson Machine Learning, IBM Watson Studio is a leading data science and machine learning platform built from the ground up for an AI-powered business. It helps enterprises simplify the process of experimentation to deployment, speed data exploration and model development and training, and scale data science operations across the lifecycle.
Lia's creator Soul Machines is developing digital humans, complete with digital brains, who are portrayed by actual humans. Verizon's Labs showcases innovators like Soul Machines to explore how 5G networks support cutting edge technology that contributes to the betterment of society. Having the speed and bandwidth of a 5G connection is critical to ensuring that digital interactions feel humanized. In human-to-human engagement, the brain rapidly identifies and processes data points such as tone and non-verbal cues. In digital-to-human engagement, mimicking human-like interactions requires 5G's bandwidth and speed.
Data scientists are expected to know a lot -- machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning. Within those areas there are dozens of languages, frameworks, and technologies data scientists could learn. How should data scientists who want to be in demand by employers spend their learning budget? I scoured job listing websites to find which skills are most in demand for data scientists. I looked at general data science skills and at specific languages and tools separately.