Torts Law



In-Stream Processing @CloudExpo @robinAKAroblimo #BigData #AI #BI #DX

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Data Nuggets in the Stream A retailer may do 100,000 POS credit card transactions per day. Opening more registers as soon as checkout volume starts to trend upwards makes customers happy. It may not be long until we have good enough AI (or at least pseudo-AI), and good enough voice recognition to replace human customer service workers, but right now nothing beats a knowledgeable employee with the authority to actually make things right for a customer who has gotten a defective product or poor service of some sort. If you're doing high-frequency stock buying and selling, making hundreds or thousands of trades per minute (or even per second in some cases), you must be able to process data and make decisions - or have a program that makes decisions - fast enough that the length of your connection to the stock exchange can make a noticeable difference in your profits -- which is pretty darn fast.


AI Helps Manufacturers Identify Product Defects – NVIDIA Developer News Center

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A California-based startup called Instrumental developed an intelligent AI inspection system to help manufactures identify product defects on the assembly line. Instrumental makes a hardware box that goes on the assembly line and takes a photo of every device that passes through and they recently announced their deep learning software called Detect which highlights units that appear defective or anomalous, giving our customers a significant edge in discovering and resolving product issues. Using TITAN X GPUs and cuDNN with the TensorFlow deep learning framework, they are able to process hundreds of units in seconds and identify the most interesting units to review. According to their blog, an engineer using Detect remotely caught an assembly process issue still in progress on the line and was able to inform the factory to correct it right away.


Zurich Insurance Deploys Robots for Personal Injury Claims Handling

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"We recently introduced AI claims handling … and saved 40,000 work hours, while speeding up the claim processing time to five seconds," Tom de Swaan told Reuters, after the insurer started using machines in March to review paperwork, such as medical reports. "We absolutely plan to expand the use of this type of AI (artificial intelligence)," he said. De Swaan said Zurich Insurance, Europe's fifth-biggest insurer, would increasingly use machine learning, or AI, for handling claims. De Swaan said he does not fear competition from tech giants like Google-parent Alphabet or Apple entering the insurance market, although some technology companies have expressed interest in cooperating with Zurich.


AI adopted by Zurich Insurance to assess personal injury claims

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"We recently introduced AI claims handling ... and saved 40,000 work hours, while speeding up the claim processing time to five seconds," Tom de Swaan said, after the insurer started using machines in March to review paperwork, such as medical reports. De Swaan said Zurich Insurance, Europe's fifth-biggest insurer, would increasingly use machine learning, or AI, for handling claims. De Swaan said he does not fear competition from tech giants like Google-parent Alphabet or Apple entering the insurance market, although some technology companies have expressed interest in cooperating with Zurich. "None of the technology companies so far have taken insurance risk on their balance sheet, because they don't want to be regulated," he said.


Zurich embraces artificial intelligence

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Zurich Insurance is introducing artificial intelligence to help decide personal injury claims. The insurance giant is rolling out the technology after processing time was cut from an hour to only seconds during a trial, according to a Reuters report. "We recently introduced AI claims handling … and saved 40,000 work hours, while speeding up the claim processing time to five seconds," Zurich chairman Tom de Swaan told Reuters. De Swaan said that Zurich would continue to increase the role of AI in claims handling.


Zurich Insurance deploying robots to decide personal injury claims

The Japan Times

ZURICH – Zurich Insurance is deploying artificial intelligence (AI) in deciding personal injury claims after test trials cut the processing time from an hour to just seconds, its chairman said. "We recently introduced AI claims handling … and saved 40,000 work hours, while speeding up the claim processing time to five seconds," Tom de Swaan said in an interview after the insurer started using machines in March to review paperwork, such as medical reports. De Swaan said Zurich Insurance, Europe's fifth-biggest insurer, would increasingly use machine learning for handling claims. De Swaan said he does not fear competition from tech giants like Google-parent Alphabet or Apple entering the insurance market, although some technology companies have expressed interest in cooperating with Zurich.


In-Stream Processing @CloudExpo @robinAKAroblimo #BigData #AI #BI #DX

#artificialintelligence

Data Nuggets in the Stream A retailer may do 100,000 POS credit card transactions per day. Opening more registers as soon as checkout volume starts to trend upwards makes customers happy. It may not be long until we have good enough AI (or at least pseudo-AI), and good enough voice recognition to replace human customer service workers, but right now nothing beats a knowledgeable employee with the authority to actually make things right for a customer who has gotten a defective product or poor service of some sort. If you're doing high-frequency stock buying and selling, making hundreds or thousands of trades per minute (or even per second in some cases), you must be able to process data and make decisions - or have a program that makes decisions - fast enough that the length of your connection to the stock exchange can make a noticeable difference in your profits -- which is pretty darn fast.


The future of robotics and artificial intelligence in Europe

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Given the great public attention currently devoted to robotics and artificial intelligence (AI) this report is very timely and points at some crucial issues that need to be addressed. Our strategy identifies Robotics and Artificial intelligence as cornerstone technologies, it addresses the new legal challenges related to robots, autonomous and AI-based systems, and it highlights issues related to skills. Recently, we published our Communication on Building a European Data Economy, which includes a comprehensive section on liability and puts forward possible mitigation measures. In the Commission, we have already investigated safety issues from various angles: we have been funding research and development with the goal of making robotics systems inherently safe, and our programme also includes a specific focus on safety certification and related standardisation.


Data-Efficient Deep Learning with G-CNNs – Scyfer

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Scyfer Active Learning Platform: once integrated, our system will passively observe the work of a domain expert (whether that's a medical doctor diagnosing patients or a factory worker identifying defective products). Data-efficient deep networks: by building in prior knowledge, like "a rotated teddy bear is still a teddy bear", we can drastically reduce the number of examples required to learn a new concept. Scyfer Active Learning Platform: once integrated, our system will passively observe the work of a domain expert (whether that's a medical doctor diagnosing patients or a factory worker identifying defective products). Scyfer Active Learning Platform: once integrated, our system will passively observe the work of a domain expert (whether that's a medical doctor diagnosing patients or a factory worker identifying defective products).