A godfather of AI is among hundreds of tech bosses and academics calling for an international treaty to avoid the technology's'catastrophic' risk to humanity. On the eve of the AI Safety Summit, Turing award winner Yoshua Bengio has signed an open letter warning the danger it poses'warrants immediate and serious attention'. It cites a survey that found over half of AI researchers estimate there is more than a 10 per cent chance advances in machine learning could lead to human extinction. Notably, among the signatories is one of China's leading AI academics, Professor Yui Zeng, a key representative of Beijing who is set to lead one of the sessions at the event in Bletchley Park. Government officials may well see his backing as a positive signal that China – whose invitation to the summit has proven highly controversial – is willing to cooperate on international regulation.

Serial tech entrepreneur Ilir Sela started Slice in 2010 with the belief that local pizzerias deserve all of the advantages of major franchises without compromising their independence. Starting with his family's pizzerias, we now empower over 18,000 restaurants (that's nearly triple Domino's U.S. network!) with the technology, services, and collective power that owners need to better serve their digitally minded customers and build lasting businesses. We're growing and adding more talent to help fulfill this valuable mission. That's where you come in. The Challenge to Solve Build up a picture of Slice target users based on their needs, wants, motivations and pain-points.

Clarifai is a leading, full-lifecycle deep learning AI platform for computer vision, natural language processing, and audio recognition. We help organizations transform unstructured images, video, text, and audio data into structured data at a significantly faster and more accurate rate than humans would be able to do on their own. Founded in 2013 by Matt Zeiler, Ph.D. Clarifai has been a market leader in AI since winning the top five places in image classification at the 2013 ImageNet Challenge. Clarifai continues to grow with employees remotely based throughout the United States and in Tallinn, Estonia. We have raised $100M in funding to date, with $60M coming from our most recent Series C, and are backed by industry leaders like Menlo Ventures, Union Square Ventures, Lux Capital, New Enterprise Associates, LDV Capital, Corazon Capital, Google Ventures, NVIDIA, Qualcomm and Osage.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

SoundHound Inc. builds voice AI products that make it easier for people to engage with the world around them. We do this by creating custom voice assistants that make it possible for companies to extend their brand in new and meaningful ways. Today, our customized voice AI solutions allow people to talk to phones, cars, smart speakers, mobile apps, coffee machines, and every other part of the emerging'voice-first' world. We are looking for an experienced Research Scientist to join our core speech team. We work on areas including automatic speech recognition (ASR), natural language processing (NLP), Machine Learning, speech synthesis (TTS), voice biometrics, pattern recognition, digital signal processing (DSP), and speech enhancement.

Humans observe the world through a combination of different modalities, like vision, hearing, and our understanding of language. Machines, on the other hand, interpret the world through data that algorithms can process. So, when a machine "sees" a photo, it must encode that photo into data it can use to perform a task like image classification. This process becomes more complicated when inputs come in multiple formats, like videos, audio clips, and images. "The main challenge here is, how can a machine align those different modalities? As humans, this is easy for us. We see a car and then hear the sound of a car driving by, and we know these are the same thing. But for machine learning, it is not that straightforward," says Alexander Liu, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and first author of a paper tackling this problem.

For U.S. Candidates Only: SRA has adopted a COVID-19 vaccination policy to safeguard the health and well-being of our employees and visitors. As a condition of employment, all employees based in the U.S. are required to be fully vaccinated for COVID-19, unless a reasonable accommodation is approved or as otherwise required by law. Incumbent must make themselves available during core business hours. This position requires the incumbent to travel for work 10% of the time.

Telco networks and systems evolved over the years to deal with novel services. Today, they are highly complex, distributed ecosystems composed of very diverse sub-environments (see Figure 1). They include myriad types of devices, connectivity means, protocols, and infrastructures often managed by different teams with varying expertise and tools, or even different companies. High-level view of the complexity of telcos' networks and systems with a large variety of devices, connectivity means, protocols, and infrastructures. Traditional network management solutions (for example, network over-provisioning, rule-based systems, reactive approaches) are reaching their limits in dealing with this complex ecosystem.

More than two dozen artificial intelligence experts from business and academia, including Texas McCombs, explored the importance of understanding how machine learning systems arrive at their conclusions so humans can trust those results. Although AI is more than 50 years old, "deep learning has been a mini-scientific revolution" since the 2010s, said one keynote speaker, Charles Elkan, a professor of computer science at the University of California, San Diego. Alice Xiang, a lawyer and a senior research scientist for Sony Group, said, "I see explainability as an important part of providing transparency and, in turn, enabling accountability." She noted the challenge of black boxes, citing as examples drug-sniffing dogs, whose abilities are mysterious but highly accurate, and the horse Clever Hans, who appeared to understand math but was really following cues from its owner. In a panel discussion called "Adopting AI," James Guszcza, a behavioral research affiliate at Stanford University and chief data scientist on leave from Deloitte LLP, said: "I think one of the previous speakers said we need to be interdisciplinary; I take it a little bit further and say we need to be transdisciplinary."

The highly trained algorithm combed through a defined dataset to accurately predict new structures that could fuel processes in clean energy, chemical and automotive industries. According to Mirkin, what makes this so important is the access to unprecedentedly large, quality datasets because machine learning models and AI algorithms can only be as good as the data used to train them. But the loosely synonymous "materials genome" includes nanoparticle combinations of any of the usable 118 elements in the periodic table, as well as parameters of shape, size, phase morphology, crystal structure and more. Machine learning applications are ideally suited to tackle the complexity of defining and mining the materials genome, but are gated by the ability to create datasets to train algorithms in the space. "As these data suggest, the application of machine learning, combined with Megalibrary technology, may be the path to finally defining the materials genome," said Joseph Montoya, senior research scientist at TRI. Identifying new green catalysts will enable the conversion of waste products and plentiful feedstocks to useful matter, hydrogen generation, carbon dioxide utilization and the development of fuel cells.