One of the exciting things about Artificial Intelligence is the steady stream of new accomplishments that we see in the news. Every week, some research institution or company accomplishes something amazing with AI, whether it is translating a long lost language, or building a massive model, the scale of which has never been done before.
But what does it all mean? If I am a business CEO, what impact if any does this have on my business? Is there any way I can leverage it? If I am a teacher, what should I tell my students? Being aware of recent events is always a good thing, but without context it is hard to make sense of them.
In this article, we dissect this specific announcement, and answer seven high level questions you may have about it. Read More
Monthly Archives: July 2021
The self-driving race between Elon Musk’s Tesla and Domino’s pizza robots
- Tesla and Elon Musk are pressing ahead with full self-driving even amid blown deadlines, safety issues, and multi-billion-dollar efforts from Alphabet-backed Waymo and GM-backed Cruise continue.
- But some autonomous vehicle start-ups are making big bets the future will be smaller, ‘zero-occupant’ vehicles including three-wheelers that deliver pizza.
- Refraction AI and Nuro are among the delivery technology innovators working with local restaurants in Texas and corporations including Domino’s Pizza.
First-Generation Inference Accelerator Deployment at Facebook
In this paper, we provide a deep dive into the deployment of inference accelerators at Facebook. Many of our ML workloads have unique characteristics, such as sparse memory accesses, large model sizes, as well as high compute, memory and network bandwidth requirements. We co-designed a high-performance, energy-efficient inference accelerator platform based on these requirements. We describe the inference accelerator platform ecosystem we developed and deployed at Facebook: both hardware, through Open Compute Platform (OCP), and software framework and tooling, through Pytorch/Caffe2/Glow. A characteristic of this ecosystem from the start is its openness to enable a variety of AI accelerators from different vendors. This platform, with six low-power accelerator cards alongside a single socket host CPU, allows us to serve models of high complexity that cannot be easily or efficiently run on CPUs. We describe various performance optimizations, at both platform and accelerator level, which enables this platform to serve production traffic at Facebook. We also share deployment challenges, lessons learned during performance optimization, as well as provide guidance for future inference hardware co-design. Read More
#performanceAI voice actors sound more human than ever—and they’re ready to hire
A new wave of startups are using deep learning to build synthetic voice actors for digital assistants, video-game characters, and corporate videos.
The company blog post drips with the enthusiasm of a ’90s US infomercial. WellSaid Labs describes what clients can expect from its “eight new digital voice actors!” Tobin is “energetic and insightful.” Paige is “poised and expressive.” Ava is “polished, self-assured, and professional.”
Each one is based on a real voice actor, whose likeness (with consent) has been preserved using AI. Companies can now license these voices to say whatever they need. They simply feed some text into the voice engine, and out will spool a crisp audio clip of a natural-sounding performance. Read More
Google News Initiative launches AI Academy for Small Newsrooms
The first pilot edition of the AI Academy for Small Newsrooms program will commence in September 2021 and will welcome journalists and developers from small news organizations in Europe, Middle East, and Africa (EMEA) region.
The Google News Initiative has partnered with Polis, the journalism think-tank of the London School of Economics and Political Science (LSE), to launch the “AI Academy for Small Newsrooms” , a six-week-long, free online program for 20 media professionals to learn how artificial intelligence (AI) can be used to support their journalism.
The program combines a series of masterclasses given by experts working at the intersection of journalism and artificial intelligence with opportunities for discussion among participants. Read More
XAI: Learning Fairness with Interpretable Machine
Federated Learning and the Future of ML
By sharing ML models and training data, organisations can power-up their ML projects. Now there’s a way to do it without compromising data privacy or security
Amazing things happen when different organisations work together. It’s something that we see in business and technology, where collaboration has often helped drive new ideas or product categories forwards, and something we’ve seen over the last year or so in medicine and science, as scientists, institutions and pharmaceutical companies have worked together to fight the COVID-19 pandemic. Now, though, collaboration could also prove crucial to harnessing the power of machine learning and AI, in turn fuelling further developments in medicine, business, technology and science, but only if organisations can find a secure way to share data. To be more specific, they need a way for their machine learning models to train using data from a wider range of datasets, while reducing the risk of compromising the privacy or security of the data.
Machine learning is already revolutionising fields as diverse as finance, security, public services, manufacturing and transportation. It’s helping doctors to spot and diagnose conditions, fraud investigators to uncover money laundering and city transport planners to optimise their transport systems. But before machine learning models can analyse streams of data and a problem or recommend an action, they need to be trained using existing datasets. Generally speaking, the more data they have to work with, the more accurate and useful their models will be. Read More
China’s Tencent Says It’ll Use Face Recognition to Keep Minors From Gaming at Night
henzhen, China-based gaming giant Tencent has announced it will use a face recognition system to prevent minors in its home country from playing video games late into the night.
Tencent is attempting to keep ahead of recent regulations designed to stamp out what the Chinese government defines as excessive and unhealthy gaming habits. In 2019, China passed a law ostensibly intended to prevent minors “from indulging in online games.” According to NPR, that includes a ban on minors playing video games from 10:00 p.m. to 8:00 a.m., as well as limiting their playtime to 90 minutes a day. The law also prohibited minors from spending more than $28 to $57 a month on micro-transactions. New rules requiring all individuals, regardless of age, to register for games using their real identities and prohibiting citizens from playing games that include “sexual explicitness, goriness, violence, and gambling” were also implemented. Read More
How many robots does it take to run a grocery store?
Facial recognition tech has been widely used across the US government for years, a new report shows
As George Floyd’s death sparked protests in cities across the country, six federal agencies turned to facial-recognition software in an effort to identify people in images of the civil unrest, according to a new report from a government agency.
The agencies used facial recognition software from May to August of last year “to support criminal investigations related to civil unrest, riots, or protests,” according to a report released on Tuesday by the US Government Accountability Office, based on a survey of 42 federal agencies. The US Postal Inspection Service, for instance, told the GAO that it used software from Clearview AI, a controversial facial-recognition system, to help track down people suspected of crimes, such as stealing and opening mail and stealing from Postal Service buildings. Read More
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