In training AI systems, games are a good proxy for real-world tasks. "A general game-playing agent could, in principle, learn a lot more about how to navigate our world than anything in a single environment ever could," says Michael Bernstein, an associate professor of computer science at Stanford University, who was not part of the research. "One could imagine one day rather than having superhuman agents which you play against, we could have agents like SIMA playing alongside you in games with you and with your friends," says Tim Harley, a research engineer at Google DeepMind who was part of the team that developed the agent. The team trained SIMA on lots of examples of humans playing video games, both individually and collaboratively, alongside keyboard and mouse input and annotations of what the players did in the game, says Frederic Besse, a research engineer at Google DeepMind. Then they used an AI technique called imitation learning to teach the agent to play games as humans would.

Increased capabilities such as recognition and self-adaptability are now required from IoT applications. While IoT node power consumption is a major concern for these applications, cloud-based processing is becoming unsustainable due to continuous sensor or image data transmission over the wireless network. Thus optimized ML capabilities and data transfers should be integrated in the IoT node. Moreover, IoT applications are torn between sporadic data-logging and energy-hungry data processing (e.g. image classification). Thus, the versatility of the node is key in addressing this wide diversity of energy and processing needs. This paper presents SamurAI, a versatile IoT node bridging this gap in processing and in energy by leveraging two on-chip sub-systems: a low power, clock-less, event-driven Always-Responsive (AR) part and an energy-efficient On-Demand (OD) part. AR contains a 1.7MOPS event-driven, asynchronous Wake-up Controller (WuC) with a 207ns wake-up time optimized for sporadic computing, while OD combines a deep-sleep RISC-V CPU and 1.3TOPS/W Machine Learning (ML) for more complex tasks up to 36GOPS. This architecture partitioning achieves best in class versatility metrics such as peak performance to idle power ratio. On an applicative classification scenario, it demonstrates system power gains, up to 3.5x compared to cloud-based processing, and thus extended battery lifetime.

At DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know. The Applied team collaborates closely with a wide variety of teams across Google/Alphabet, leveraging DeepMind expertise to deploy advanced machine learning algorithms with the goal of improving Alphabet products and services. We are a driven, collaborative, diverse team based in London and Mountain View.

Rosebud.ai is building the AI Canva. We first build the AI generated stock photo platform to replace Getty and Shutterstock. We're the team that built Tokkingheads (2 million IOS downloads, all organic) that allows any portrait/photo/face to be animated in seconds with no skill. Our team is still quite small ( 10), so you'll have massive impact on the trajectory of the business from Day One.

We are looking for qualified Computer Vision Research Engineers for eKYC project, who will help us build up our digital identity verification products. We are an AI Fintech company specialized in assessing credit profiles of consumers in emerging markets combining pioneering AI with large alternative data sources. In 2020 we reached our ambitious milestone of credit profiling 1 billion consumers spanning 4 countries - Vietnam, Indonesia, India & the Philippines - and building a platform for the wider industry and the financial services industry, in particular, to provide the "un & under" served access to credit. At the core of this initiative has been our strict and unwavering adherence to the norms of consumer data privacy and consumer data rights. But we're not satisfied as we embark on the next leg of our journey to deliver 100 million credit lines to consumers in the markets where we operate.

Research Engineer, Machine/Deep Learning and Network Security in Electrical & Electronic Engineering, Research Related with NATIONAL UNIVERSITY OF …

SirionLabs - SaaS Product firm is looking for people who are driven to make a difference. Bringing together leading innovation, unrivaled Contract Lifecycle Management expertise, and a deep commitment to customer success, SirionLabs helps the world's leading businesses contract smarter. Powered by intelligence uniquely connected across the complete contract lifecycle. SirionLabs' easy-to-use, highly configurable Smarter Contracting Platform brings legal, procurement, and business teams together to author stronger agreements, manage risk and strengthen counterparty relationships. Today, analyst firms such as Forrester, Spend Matters and IDC agrees that SirionLabs is a leader in CLM whilst world-leading businesses including Vodafone, Unilever, DHL, and Morgan Stanley trust SirionLabs to create, control, and manage over 5m contracts worth more than $300bn, in 100 countries around the world.

We have an position for Artificial Intelligence engineer in Centre of Excellence in Modelling and Simulation for Next Generation Ports, National University of Singapore (NUS). C4NGP aims to become the leading go-to global research centre in Modelling & Simulation in Next Generation Ports and Maritime Systems. This position provides an exciting opportunity to work with a dynamic team striving to achieve excellence in modelling, simulation, optimization and analytics for the next generation ports and maritime systems. We are seeking a passionate and self-motivated research Artificial Intelligence engineer to join our team to work on logistics/maritime industry projects and support C4NGP investigators in their project management efforts. At NUS, the health and safety of our staff and students are one of our utmost priorities, and COVID-vaccination supports our commitment to ensure the safety of our community and to make NUS as safe and welcoming as possible.

… and distributed SLAM, c) deep learning in safety and security, d) collaborative brain-machine intelligence, and e) robotic human augmentation.

At DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives, and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, maternity or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know. At DeepMind, we've built a unique culture and work environment where long-term ambitious research can flourish. Our special interdisciplinary team combines the best techniques from deep learning, reinforcement learning and systems neuroscience to build general-purpose learning algorithms.