San Francisco, March 3, 2021 -- BrainChip Holdings Ltd. (ASX: BRN), a leading provider of ultra-low-power, high-performance AI technology, ended the 2020 calendar year having made significant strides in the development of its technology backed by the launch of its Early Access Program (EAP), availability of Akida evaluation boards, new partnerships, and expansion of its executive leadership and global facilities. The Company's EAP was launched in June targeting specific customers in a diverse set of end markets in order to ensure availability of initial devices and evaluation systems for key applications. Multiple customers have committed to the advanced purchase of evaluation systems for a range of strategic Edge applications including Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV), Unmanned Aerial Vehicles (UAV), Edge vision systems and factory automation. Among those joining the EAP include VORAGO Technologies in a collaboration intended to support a Phase I NASA program for a neuromorphic processor that meets spaceflight requirements. BrainChip is also collaborating with Tier-1 Automotive Supplier Valeo Corporation to develop neural network processing solutions for ADAS and AV.
In June 2020, a new and powerful artificial intelligence (AI) began dazzling technologists in Silicon Valley. Called GPT-3 and created by the research firm OpenAI in San Francisco, California, it was the latest and most powerful in a series of'large language models': AIs that generate fluent streams of text after imbibing billions of words from books, articles and websites. GPT-3 had been trained on around 200 billion words, at an estimated cost of tens of millions of dollars. The developers who were invited to try out GPT-3 were astonished. "I have to say I'm blown away," wrote Arram Sabeti, founder of a technology start-up who is based in Silicon Valley. "It's far more coherent than any AI language system I've ever tried. All you have to do is write a prompt and it'll add text it thinks would plausibly follow. I've gotten it to write songs, stories, press releases, guitar tabs, interviews, essays, technical manuals. I feel like I've seen the future."
We provide powerful insights into the world by extracting information from satellite imagery fully automatically. A scalable artificial intelligence builds the core of the blackshark platform, detecting features with incredible precision and speed. A novel machine learning approach was developed to train a neural network for new features rapidly, enabling blackshark to serve various use cases in many different industries. We can also can also fully automatically and semantically reconstruct detected features in 3D, using a patented technology for efficiently storing and streaming petabytes of data, as showcased in our work on the Microsoft Flight Simulator. Our team of 50 data scientists, geospatial engineers, 3D rendering programmers and developers, self-funded and based in San Francisco, US, and Graz, Austria, Europe's computer vision hub.
The idea of creating a virtual human that can converse seamlessly with a user seems daunting to most people who are just getting into artificial intelligence and looking into how utterly complex existing commercial systems are. And their fears aren't misled - larger systems that contain a plethora of data samples and an intricate network architecture, and are responsible for providing the highest quality home assistant system are very difficult to replicate. But, creating virtual assistants at a smaller level has already been simplified to allow virtually anyone to make their own conversational persona. Over the past decade, the University of Southern California's Institute for Creative Technologies has developed countless virtual personalities for a variety of reasons: The institute has been able to create the amount of virtual humans as they have because of the technology they developed titled'NPCEditor'. As the name implies, the program allows the team to edit an NPC, or non-player-character. Developed by research scientist Anton Leuski and lead professor of NLP David Traum, the software has been simplified enough so that it is incredibly easy to create a virtual human.
A recent paper from the Center for Applied Data Ethics (CADE) at the University of San Francisco urges AI practitioners to adopt terms from anthropology when reviewing the performance of large machine learning models. The research suggests using this terminology to interrogate and analyze bureaucracy, states, and power structures in order to critically assess the performance of large machine learning models with the potential to harm people. "This paper centers power as one of the factors designers need to identify and struggle with, alongside the ongoing conversations about biases in data and code, to understand why algorithmic systems tend to become inaccurate, absurd, harmful, and oppressive. This paper frames the massive algorithmic systems that harm marginalized groups as functionally similar to massive, sprawling administrative states that James Scott describes in Seeing Like a State," the author wrote. The paper was authored by CADE fellow Ali Alkhatib, with guidance from director Rachel Thomas and CADE fellows Nana Young and Razvan Amironesei. The researchers particularly look to the work of James Scott, who has examined hubris in administrative planning and sociotechnical systems.
Organisations, regardless of size, are adopting emerging technologies like machine learning, data science, and AI to gain meaningful insights from large chunks of data in a bid to accelerate their growth. According to the Analytics and Data Science India Industry study 2020, advanced analytics, predictive modelling, and data science together account for 16% of the analytics revenues across enterprises. The rapid digital adoption has opened the skill gap wide. Many institutions across the world are now offering courses -- both online and offline -- to plug this gap. Here are the top ten Master's in Machine Learning in the US.
Systems designed to detect deepfakes--videos that manipulate real-life footage via artificial intelligence--can be deceived, computer scientists showed for the first time at the WACV 2021 conference which took place online Jan. 5 to 9, 2021. Researchers showed detectors can be defeated by inserting inputs called adversarial examples into every video frame. The adversarial examples are slightly manipulated inputs which cause artificial intelligence systems such as machine learning models to make a mistake. In addition, the team showed that the attack still works after videos are compressed. "Our work shows that attacks on deepfake detectors could be a real-world threat," said Shehzeen Hussain, a UC San Diego computer engineering Ph.D. student and first co-author on the WACV paper.
AI/ML Job: Data Scientist, Growth Data Scientist, Growth at Pinterest United States › California › San Francisco (Posted Jan 25 2021) Job description Millions of people across the world come to Pinterest to find new ideas every day. It's where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love. In your role, you'll be challenged to take on work that upholds this mission and pushes Pinterest forward. You'll grow as a person and leader in your field, all the while helping Pinners make their lives better in the positive corner of the internet.
Weeks after revealing the company planned to turn the Zestimate into a live offer in certain Zillow Offers markets, Zillow announced Thursday that qualifying homeowners in 20 markets would now officially see their Zestimate turned into a live offer. The move comes roughly 15 years after Zillow initially introduced the Zestimate to consumers, a proprietary automated valuation model that uses machine learning to predict the market value of a home. "We've long said, one of the beauties of Zillow Offers is that it's the best first step for you to think about selling," Jeremy Wacksman, Zillow's chief operations officer told Inman. "Nobody wants to think about selling, most sellers are thinking about buying, they want to be thinking about buying, but at some point they have to think about, 'oh my gosh I have to go through this process.'" "The Zestimate is the starting point for that. If we can turn the Zestimate into a starting offer for certain customers, that's going to get them to raise their hand and say, 'Okay, I'm thinking about moving, help me understand, am I trading in? Can I talk to an agent about that?'"