Only 22% of CRM users say AI meaningfully helps them a lot at work and only 12% actually use a specific AI-based tool; 11% say AI in their CRM allows them to focus on high-value customers and 36% CRM think AI is a strong value for the money spent on it [Dynata and Freshworks survey of 501 U.S. CRM users] Stanford's Michael Webb has developed a new methodology to estimate the impact of AI on jobs: Matching patent applications with job descriptions, he found that "AI exposure is highest for high-skilled occupations, suggesting that AI will affect very different people than software and robots," concluding that "high-wage occupations are relatively more exposed to AI than low-wage occupations" [The Impact of Artificial Intelligence on the Labor Market] More than 60% of Americans believe the government and companies collect data about them daily; more than 80% feel they have little control over the data collected about them by the government and companies and believe the risks of this data collection outweigh the benefits; 59% say they have little or no understanding regarding what is done with the data collected about them by companies and 78% have similar lack of understanding about the government's data collection; 70% say their personal data is less secure than 5 years ago [Pew Research Center] Over 74% of consumers are still confused about how their data is handled; over 49% still don't trust the privacy policies that businesses have shared; 32% of consumers are "Privacy Actives"--they care about privacy, are willing to act to protect it, and have already acted by switching companies or providers based on their data-sharing practices. European startups pursuing some kind of AI-related product or service are on track to raise $4.9 billion in 2019, up from $3.2 billion in 2018. Funding this year for robotics will reach $1.4 billion and for deep learning $320 million [Venturebeat.com] The market for virtual digital assistants (VDAs) will grow from $1.3 billion in 2018 to more than $8.9 billion in 2025 [Tractica]

Geneva and Sachseln, Switzerland, November 25, 2019 – STMicroelectronics (NYSE: STM), a global semiconductor leader serving customers across the spectrum of electronics applications, is working with maxon, a leading precision-motor provider and a member of the ST Partner Program, to accelerate the design of robotics applications and industrial servo drives. The companies will demonstrate a jointly developed servo control kit at sps 2019 trade show in Nuremberg, November 26-28 (Booth 10.1/138). The EVALKIT-ROBOT-1 is a plug-and-play solution aimed to help users easily approach the world of precise positioning and high-end motion in servo drives and robotics. A maxon 100-Watt BLDC motor with built-on 1024-pulse incremental encoder is included in the kit, embodying the company's expertise in magnetic design in motors that ensures smoothness and balance to allow fine control even at low rotor speeds. The servo control board supplied with the kit contains ST's STSPIN32F0A intelligent 3-phase motor controller and a complete inverter stage built with ST power transistors ready to connect to the motor.

When it comes to AI (Artificial Intelligence), there's usually a major focus on using large datasets, which allow for the training of models. What may seem like a robust dataset could instead be highly skewed, such as in terms of race, wealth and gender. Then what can be done? Well, to help answer this question, I reached out to Dr. Rebecca Parsons, who is the Chief Technology Officer of ThoughtWorks, a global technology company with over 6,000 employees in 14 countries. She has a strong background in both the business and academic worlds of AI.

Imagine that you have been convicted of a crime and an algorithm is to help the judge by proposing the sentence. When computers are programmed, discrimination is built into the algorithms, so if you look a certain way you get a harder sentence. This example is not fiction. It comes from the USA where AI is being used in the legal system to propose sentencing for criminal offences, and it proposes harder sentences for black people. It might be chance, a prejudiced system developer or perhaps distorted data that the system had to practice on.

Charge prediction which aims to determine appropriate charges for criminal cases based on textual fact descriptions, is an important technology in the field of AI&Law. Previous works focus on improving prediction accuracy, ignoring the interpretability, which limits the methods' applicability. In this work, we propose a deep neural framework to extract short but charge-decisive text snippets - rationales - from input fact description, as the interpretation of charge prediction. To solve the scarcity problem of rationale annotated corpus, rationales are extracted in a reinforcement style with the only supervision in the form of charge labels. We further propose a dynamic rationale attention mechanism to better utilize the information in extracted rationales and predict the charges. Experimental results show that besides providing charge prediction interpretation, our approach can also capture subtle details to help charge prediction.

Past literature has been effective in demonstrating ideological gaps in machine learning (ML) fairness definitions when considering their use in complex socio-technical systems. However, we go further to demonstrate that these definitions often misunderstand the legal concepts from which they purport to be inspired, and consequently inappropriately co-opt legal language. In this paper, we demonstrate examples of this misalignment and discuss the differences in ML terminology and their legal counterparts, as well as what both the legal and ML fairness communities can learn from these tensions. We focus this paper on U.S. anti-discrimination law since the ML fairness research community regularly references terms from this body of law.

In the last two years, more than 200 papers have been written on how machine learning (ML) systems can fail because of adversarial attacks on the algorithms and data; this number balloons if we were to incorporate papers covering non-adversarial failure modes. The spate of papers has made it difficult for ML practitioners, let alone engineers, lawyers, and policymakers, to keep up with the attacks against and defenses of ML systems. However, as these systems become more pervasive, the need to understand how they fail, whether by the hand of an adversary or due to the inherent design of a system, will only become more pressing. In order to equip software developers, security incident responders, lawyers, and policy makers with a common vernacular to talk about this problem, we developed a framework to classify failures into "Intentional failures" where the failure is caused by an active adversary attempting to subvert the system to attain her goals; and "Unintentional failures" where the failure is because an ML system produces an inherently unsafe outcome. After developing the initial version of the taxonomy last year, we worked with security and ML teams across Microsoft, 23 external partners, standards organization, and governments to understand how stakeholders would use our framework. Throughout the paper, we attempt to highlight how machine learning failure modes are meaningfully different from traditional software failures from a technology and policy perspective.

One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively small number of relevant documents as the initial source for such content. This line of research yielded moderate success, which is of limited use in a real-world system. Furthermore, for such a system to yield a comprehensive set of relevant arguments, over a wide range of topics, it requires leveraging a large and diverse corpus in an appropriate manner. Here we present a first end-to-end high-precision, corpus-wide argument mining system. This is made possible by combining sentence-level queries over an appropriate indexing of a very large corpus of newspaper articles, with an iterative annotation scheme. This scheme addresses the inherent label bias in the data and pinpoints the regions of the sample space whose manual labeling is required to obtain high-precision among top-ranked candidates. 1 Introduction Starting with the seminal work of Mochales Palau and Moens (2009), argument mining has mainly focused on the following tasks - identifying argumentative text segments within a given document; labeling these text segments according to the type of argument and its stance; and elucidating the discourse relations among the detected arguments. Typically, the considered documents were argumentative in nature, taken from a well defined domain, such as legal documents or student essays. More recently, some attention had been given to the corresponding retrieval task - given a controversial topic, retrieve arguments with a clear stance towards this topic. This is usually done by first retrieving - manually or automatically - documents relevant to the topic, and then using argument mining techniques to identify relevant argumentative segments therein. This documents-based approach was originally explored over Wikipedia (Levy et al. 2014; Rinott et al. 2015), and more recently over the entire Web (Stab et al. 2018). For an argument retrieval system to be of practical use requires: (1) high precision, and (2) wide coverage.

This is an excerpt from the free ebook "Predictive Analytics for Business: How to use recommender systems, dynamic pricing, and churn prediction to drive business results". Artificial intelligence is finding its way into more industries, and a growing number of companies already experience the benefits of implementing AI. Even though AI is developing and gaining more popularity, many businesses still can't find their way with this "new" technology. There's a number of reasons why a company may fear AI implementation. In 2019, O'Reilly published an ebook summarizing the findings of their surveys concerning AI adoption in enterprises and listed some of the most common factors that hold back further AI implementation. Other reasons include lack of data and lack of skilled people and difficulties identifying appropriate business cases, among others. As you can see above, some of the common problems mostly include those related to people, data, or business alignment. While every company is different and will experience the process of AI adoption in a different way as well, there are certain hurdles you should be aware of.

The City of Tampere in Finland and Tieto, a leading Nordic IT services and software company based in Espoo, have developed a solution that uses artificial intelligence (AI) and Internet of Things (IoT) technology to improve the safety of pedestrians in urban traffic. Global urbanisation increases the number of people on the move in metropolitan areas, but as road traffic increases, so does the risk of accidents, especially at intersections, with the risk of injury and death being especially high for pedestrians. To increase the safety of urban traffic and prevent accidents, the City of Tampere and Tieto have built a pilot system that uses AI and IoT technology to automatically detect when a pedestrian is planning to cross the street at an intersection. Then an alert can be relayed to automatic traffic signs, and in the future directly to vehicles themselves, providing a key building block for connected and autonomous transport. Developed as a part of the Smart Tampere development program's 6Aika CityIoT project, the pilot system has been built in such a way that prevents the identification of individuals or vehicles to comply with the country's strict privacy laws.