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Would You Trust a Lawyer Bot With Your Legal Needs?

WSJ.com: WSJD - Technology

Would you entrust a personal-injury claim, divorce settlement or high-stakes contract to an algorithm? A growing number of apps and digital services are betting you will, attracting millions of Silicon Valley investment dollars but raising questions about the limits and ethics of technology in the legal sphere. Among the leaders in the emergent robo-lawyering field is DoNotPay, an app dreamed up by Joshua Browder in 2015, when he was a 17-year-old Stanford University student, to help friends dispute parking tickets. The app, which relies on an artificial intelligence-enabled chatbot, became popular, and has expanded its focus to other consumer legal services. In June it hit the million-case mark, helping save people upward of $30 million since it started, Mr. Browder says. It raised a new $12 million round of funding the same month.


Data Scientist - Legal - IoT BigData Jobs

#artificialintelligence

The Data Scientist is responsible for litigation analysis, benchmarking of product liability claims, plaintiff's attorneys, and defense attorneys, and identifying emerging vehicle issues within the privileged legal database and the safety databases by analyzing, mining, and monitoring relevant data. This requires identifying trends and patterns in Team Connect and other databases. Must be analytical, adaptable, and very detail oriented, producing high quality and accurate work product. Master's degree in Applied Statistics/Mathematics, Computer Science, Operations Research or related field – Ability to effectively communicate results and methodologies. The policy of General Motors is to extend opportunities to qualified applicants and employees on an equal basis regardless of an individual's age, race, color, sex, religion, national origin, disability, sexual orientation, gender identity/expression or veteran status.


AI and me: friendship chatbots are on the rise, but is there a gendered design flaw?

#artificialintelligence

Ever wanted a friend who is always there for you? Someone who will perk you up when you're in the dumps or hear you out when you're enraged? Only, she isn't called Replika. She's called whatever you like; Diana; Daphne; Delectable Doris of the Deep. Gender, voice, appearance: all are up for grabs.


AI at the edge is enabling the push toward defect-free factories

#artificialintelligence

According to several studies by Intel spanning 2018, 2019, and 2020, AI and edge computing make it possible to positively identify up to 99% of visible manufacturing defects before a product ever leaves the line. "One of the most important things manufacturers care about is product quality," says Brian McCarson, Vice President and Senior Principal Engineer, Internet of Things Group (IOTG) at Intel Corporation and a featured speaker at Transform, VentureBeat's upcoming digital conference. "Manufactures prefer throwing away fewer defective products. They strive to have less rework and fewer customer returns. They also want to reduce the cost of their operations by making their tools and processes more efficient, and improve the reliability of their machines so they can proactively do maintenance before it is too late and have more predictable uptime."


Legal Issues Raised by Deploying AI in Healthcare

#artificialintelligence

The theory is that the law should deal with like situations in like ways. The theory is that the law should deal with like situations in like ways. In some respects, however, Artificial Intelligence, especially the concept of machine learning, is virtually unprecedented, so the law is struggling with how to deal with it, or will be soon. Consider a few of the difficulties that the law will probably need to address: Who will pay for healthcare services dependent on AI, and who will be entitled to such payments? Will those payments be keyed to "value," the currently orthodox yardstick?


Council Post: From Computer Vision To Deep Learning: How AI Is Augmenting Manufacturing

#artificialintelligence

In the race to enable manufacturing plants to increase production in the face of an intermittent human workforce, manufacturers are looking at how to supplement their cameras with AI to give human inspectors the ability to spot defective products immediately and correct the problem. While machine vision has been around for more than 60 years, the recent surge in the popularity of deep learning has elevated this sometimes misunderstood technology to the attention of major manufacturers globally. As CEO of a deep learning software company, I've seen how deep learning is a natural next step from machine vision, and has the potential to drive innovation for manufacturers. How does deep learning differ from machine vision, and how can manufacturers leverage this natural evolution of camera technology to cope with real-world demands? In the 1960s, several groups of scientists, many of them in the Boston area, set forth to solve "the machine vision problem."


Global Big Data Conference

#artificialintelligence

In the race to enable manufacturing plants to increase production in the face of an intermittent human workforce, manufacturers are looking at how to supplement their cameras with AI to give human inspectors the ability to spot defective products immediately and correct the problem. While machine vision has been around for more than 60 years, the recent surge in the popularity of deep learning has elevated this sometimes misunderstood technology to the attention of major manufacturers globally. As CEO of a deep learning software company, I've seen how deep learning is a natural next step from machine vision, and has the potential to drive innovation for manufacturers. How does deep learning differ from machine vision, and how can manufacturers leverage this natural evolution of camera technology to cope with real-world demands? In the 1960s, several groups of scientists, many of them in the Boston area, set forth to solve "the machine vision problem."


Data Analyst and Test Engineer, Intern - IoT BigData Jobs

#artificialintelligence

NXP Semiconductors enables secure connections and infrastructure for a smarter world, advancing solutions that make lives easier, better and safer. As the world leader in secure connectivity solutions for embedded applications, we are driving innovation in the secure connected vehicle, end-to-end security & privacy and smart connected solutions markets. NXP's Automotive business unit offers sensor and processing technology that drives all aspects of the secure connected cars of today and the autonomous cars of tomorrow. Job Summary: A 2017 summer internship Projects include: Detecting defective product outliers by applying statistical methods and machine learning algorithms to evaluate test data Analyze customer product returns through statistical methods and algorithms to determine preventive actions Develop a compilation of self-checking microcontroller flash tests that can be automated to run in its entirety or individually for bench evaluation board applications Job Qualifications: Working on a BS or MS in Electrical Engineering or Computer Science/Engineering Software: Python and C Solid understanding of Statistics and Machine learning Other skills will be supported through on the job training NXP is an Equal Opportunity/Affirmative Action Employer regardless of age, color, national origin, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, marital status, status as a disabled veteran and/or veteran of the Vietnam Era or any other characteristic protected by federal, state or local law. In addition, NXP will provide reasonable accommodations for otherwise qualified disabled individuals.


Using artificial intelligence to detect product defects with AWS Step Functions Amazon Web Services

#artificialintelligence

Factories that produce a high volume of inventory must ensure that defective products are not shipped. This is often accomplished with human workers on the assembly line or through computer vision. You can build an application that uses a custom image classification model to detect and report back any defects in a product, then takes appropriate action. This method provides a powerful, scalable, and simple solution for quality control. It uses Amazon S3, Amazon SQS, AWS Lambda, AWS Step Functions, and Amazon SageMaker.


Convolutional Recurrent Reconstructive Network for Spatiotemporal Anomaly Detection in Solder Paste Inspection

arXiv.org Machine Learning

Surface mount technology (SMT) is a process for producing printed circuit boards. Solder paste printer (SPP), package mounter, and solder reflow oven are used for SMT. The board on which the solder paste is deposited from the SPP is monitored by solder paste inspector (SPI). If SPP malfunctions due to the printer defects, the SPP produces defective products, and then abnormal patterns are detected by SPI. In this paper, we propose a convolutional recurrent reconstructive network (CRRN), which decomposes the anomaly patterns generated by the printer defects, from SPI data. CRRN learns only normal data and detects anomaly pattern through reconstruction error. CRRN consists of a spatial encoder (S-Encoder), a spatiotemporal encoder and decoder (ST-Encoder-Decoder), and a spatial decoder (S-Decoder). The ST-Encoder-Decoder consists of multiple convolutional spatiotemporal memories (CSTMs) with ST-Attention mechanism. CSTM is developed to extract spatiotemporal patterns efficiently. Additionally, a spatiotemporal attention (ST-Attention) mechanism is designed to facilitate transmitting information from the ST-Encoder to the ST-Decoder, which can solve the long-term dependency problem. We demonstrate the proposed CRRN outperforms the other conventional models in anomaly detection. Moreover, we show the discriminative power of the anomaly map decomposed by the proposed CRRN through the printer defect classification.