Google's interactive Doodle today celebrates the life and accomplishments of video game pioneer Gerald "Jerry" Lawson on what would have been his 82nd birthday. The Doodle lets you play five retro pixel-art platformers in your browser -- with two even letting you play as Lawson. The Doodles' creators want to inspire young people to follow in his footsteps, and it includes a built-in level editor and creator to nudge them on that path. Lawson was known as the "father of the video game cartridge," which he developed as Director of Engineering and Marketing at Fairchild Semiconductor. In 1976, the company released the Fairchild Channel F home console, with Lawson serving as lead developer.

Artificial intelligence (AI), machine learning and digitalisation are key words that those tasked with improving business processes in the insurance sector will be well aware of – but these tools also present the opportunity to show end customers that the insurance industry values them. Read: Amazon's online insurance store – what does it mean for the industry? Read: There will be'winners and losers' in insurance now more than ever – Guidewire This was according to Andy Fairchild, advisor and non-executive director at various insurance-related firms and owner of consultancy Julyfourth Services. Speaking during an Insurance Times webinar entitled AI: A driving force for the future of insurance yesterday (24 November 2022), in association with Inawisdom, Fairchild explained: "[AI] is how we can show customers that we value collecting their data more than we currently do. "We must, as an industry, show customers how important that data is and how important data collection for the provision of an insurance product is." AI processing of customer data and the use of AI-enabled chatbots to respond to customer queries would improve the customer experience by speeding up often slow customer journeys, said Fairchild. But the collection of data behind these operations has to be improved too. Fairchild continued: "We can get that customer data from a person-to-person interaction or – increasingly – from a person-to-machine interaction and therein lies a big move for the industry." Fairchild added that the better collection and deployment of data to construct AI models could transform customers' interactions with the insurance sector from a "trudge process" into something that "they really value". AI and machine learning also have the potential to "revolutionise" the insurance sector in terms of risk selection and pricing if data collection improves, Fairchild added. Read: Brokers embrace cloud technologies to'maintain competitive edge' He explained: "The fundamentals of our industry are risk, risk selection, the terms that we underwrite that risk selection on and the price that we put on it." However, Sameer Deshpande, head of enterprise architecture at broker PIB Group, said that the insurance sector was lagging behind other areas of financial services in its use of artificial intelligence. Deshpande explained: "There are a number of areas where [insurance is] still behind the curve – [for example,] manual processes and document processing.

Artificial intelligence (AI), machine learning and digitalisation are key words that those tasked with improving business processes in the insurance sector will be well aware of – but these tools also present the opportunity to show end customers that the insurance industry values them. Read: Amazon's online insurance store – what does it mean for the industry? Read: There will be'winners and losers' in insurance now more than ever – Guidewire This was according to Andy Fairchild, advisor and non-executive director at various insurance-related firms and owner of consultancy Julyfourth Services. Speaking during an Insurance Times webinar entitled AI: A driving force for the future of insurance yesterday (24 November 2022), in association with Inawisdom, Fairchild explained: "[AI] is how we can show customers that we value collecting their data more than we currently do. "We must, as an industry, show customers how important that data is and how important data collection for the provision of an insurance product is." AI processing of customer data and the use of AI-enabled chatbots to respond to customer queries would improve the customer experience by speeding up often slow customer journeys, said Fairchild. But the collection of data behind these operations has to be improved too. Fairchild continued: "We can get that customer data from a person-to-person interaction or – increasingly – from a person-to-machine interaction and therein lies a big move for the industry." Fairchild added that the better collection and deployment of data to construct AI models could transform customers' interactions with the insurance sector from a "trudge process" into something that "they really value". AI and machine learning also have the potential to "revolutionise" the insurance sector in terms of risk selection and pricing if data collection improves, Fairchild added. Read: Brokers embrace cloud technologies to'maintain competitive edge' He explained: "The fundamentals of our industry are risk, risk selection, the terms that we underwrite that risk selection on and the price that we put on it." However, Sameer Deshpande, head of enterprise architecture at broker PIB Group, said that the insurance sector was lagging behind other areas of financial services in its use of artificial intelligence. Deshpande explained: "There are a number of areas where [insurance is] still behind the curve – [for example,] manual processes and document processing.

In the 1970s, in the fledgling days of the video games industry, an engineer named Gerald "Jerry" Lawson designed one of the earliest game consoles, the Channel F, and also led the team that invented the game cartridge, a defining innovation in how games were made and sold. His son, Andersen Lawson, recalls that he was often working on gaming projects in the garage of their family home in Santa Clara, California. "There have been conversations recently about the struggles he might have had that were related to his colour," he says. "Was it difficult [for him]? But I never heard any grumblings from him. And I'm also certain that he earned his respect … My father was a person of colour and I think that would inspire young people today to jump in and help move the industry along."

That has been the story Silicon Valley leaders have broadcast to the world since the region first sprang into the forefront of public consciousness as the land of silicon chips, personal computers, and video games. It is an attitude in keeping with the celebration of rugged individualism and disdain for centralized political power that has been part of American political culture since the nation's founding, ideas that gained additional allure amid the stagflating malaise of the post-Vietnam, post-Watergate 1970s. In the Reagan Revolution year of 1980, the sole election-year commentary in the microelectronics-industry newsletter InfoWorld was a cartoon tucked into a bottom corner of the editorial page. "I was going to keep track of all the candidates' significant statements," one man sighed to another as they stood in front of a computer terminal, "but there's no way to process an empty disk." Four years later, Steve Jobs declared, without embarrassment, that he had never voted in his life.

Each evokes memories of the golden age of video games, which brought the first wave of consoles you could connect to your home television. But there's an oft-forgotten person from that era whose contributions to the industry still resonate today: a black engineer named Jerry Lawson. Lawson oversaw the creation of the Channel F, the first video game console with interchangeable game cartridges – something the first Atari and Magnavox Odyssey systems did not use. Those initial consoles had a selection of games hardwired into the console itself. But Lawson, an engineer and designer at Fairchild Camera and Instrument Corp., led a team at the Silicon Valley semiconductor maker charged with creating a game system using Fairchild's F8 microprocessor and storing games on cartridges.

In terms of basic research, our current focus is the development, of broadly applicable techniques for description and matching of structure in sensory data. Such techniques appear to lmderlie virtually every aspect of early and intermediate vision, such as edge and region finding, perceptual organization and grouping, and the recovery of 3-D shape from contour, texture, stereo and motion They appear to be equally important in other sensory domains, such as audition (e g, for describing the structure in spectrograms.) In particular, we are dealing with the problem of grey-level inspection, and are constructing a vision workbench to allow rapid experimentation with alternative techniques Finally, WC are examining a variety of special-purpose architectures for image processing. These range from a SUN (MC68000-based) workstation, augment,cd with high-speed pipelined VLSI components, to a massively parallel architerture involving a thousand processors and a novel interconnection network. Knowledge Representation Contact: Ronald J. Brachman Having had experience with knowledge representation syst,ems designed to support "common sense" reasoning, we are developing and implementing a new framework for representation and reasoning in arcas requiring "expertise."

Introduction This report summarizes our experience in building PIES, a knowledge-based system that diagnoses problems in semiconductor fabrication processes by analyzing parametric test data. Parametric measurement, which is performed on test circuits at the end of a complicated semiconductor fabrication process, provides semiconductor engineers with early information to monitor the "health' ' of the overall fabrication process. Typically, hundreds of measurements are made on each wafer. The problem is to reduce the resulting ream of data to a concise summary of the process status: whether the process is functioning correctly and, if not, what the nature and cause of the abnormality is. Currently, this interpretation taskis performed by a group of semiconductor specialists known as failure-analysis or yield-enhancement engineers and routinely consumes a large portion of their time. It is critical that problems be identified quickly to avoid a major operational loss.

The Parametric Interpretation Expert System (PIES) is a knowledge system for interpreting the parametric test data collected at the end of complex semiconductor fabrication processes. The system transforms hundreds of measurements into a concise statement of all the overall health of the process and the nature and probable cause of any anomalies. A key feature of PIES is the structure of the knowledge base, which reflects the way fabrication engineers reason causally about semiconductor failures. This structure permits fabrication engineers to do their own knowledge engineering, to build the knowledge base, and then to maintain it to reflect process modifications and operating experience. The approach appears applicable to other process control and diagnosis tasks.

The Fairchild Laboratory for Artificial Intelligence Research (FLAIR) was inaugurated in October, 1980, with the purposes of introduction AI Technology into Fairchild Camera and Instrument Corporation, and of broadening the AI base of its parent company, Schlumberger Ltd. The charter of the laboratory includes basic and applied research in all AI disciplines. Currently, we have significant efforts underway in several areas of computational perception, knowledge representation and reasoning, and AI-related architectures. We also engage in various tool-building activities to support our research program. The current computational environment includes several large mainframes dedicated to AI research, a number of high-performance personal scientific machines, and extensive graphics capabilities.