Technological change and innovation are vitally important, especially for high-tech companies. However, factors influencing their future research and development (R&D) trends are both complicated and various, leading it a quite difficult task to make technology tracing for high-tech companies. To this end, in this paper, we develop a novel data-driven solution, i.e., Deep Technology Forecasting (DTF) framework, to automatically find the most possible technology directions customized to each high-tech company. Specially, DTF consists of three components: Potential Competitor Recognition (PCR), Collaborative Technology Recognition (CTR), and Deep Technology Tracing (DTT) neural network. For one thing, PCR and CTR aim to capture competitive relations among enterprises and collaborative relations among technologies, respectively. For another, DTT is designed for modeling dynamic interactions between companies and technologies with the above relations involved. Finally, we evaluate our DTF framework on real-world patent data, and the experimental results clearly prove that DTF can precisely help to prospect future technology emphasis of companies by exploiting hybrid factors.

Deep learning models have been criticized for their lack of easy interpretation, which undermines confidence in their use for important applications. Nevertheless, they are consistently utilized in many applications, consequential to humans' lives, mostly because of their better performance. Therefore, there is a great need for computational methods that can explain, audit, and debug such models. Here, we use flip points to accomplish these goals for deep learning models with continuous output scores (e.g., computed by softmax), used in social applications. A flip point is any point that lies on the boundary between two output classes: e.g. for a model with a binary yes/no output, a flip point is any input that generates equal scores for "yes" and "no". The flip point closest to a given input is of particular importance because it reveals the least changes in the input that would change a model's classification, and we show that it is the solution to a well-posed optimization problem. Flip points also enable us to systematically study the decision boundaries of a deep learning classifier. The resulting insight into the decision boundaries of a deep model can clearly explain the model's output on the individual-level, via an explanation report that is understandable by non-experts. We also develop a procedure to understand and audit model behavior towards groups of people. Flip points can also be used to alter the decision boundaries in order to improve undesirable behaviors. We demonstrate our methods by investigating several models trained on standard datasets used in social applications of machine learning. We also identify the features that are most responsible for particular classifications and misclassifications.

In recent years, computer scientists have increasingly com e to recognize that artificial intelligence (AI) systems have the potential to create harmful consequences. Especially within machine learning, there have been numerous efforts to formally characterize various not ions of fairness and develop algorithms to satisfy these criteria. However, most of this research has proceede d without any nuanced discussion of ethical foundations. Partly as a response, there have been several r ecent calls to think more broadly about the ethical implications of AI (Barabas et al., 2018; Hu and Chen, 2018b; Torresen, 2018; Green, 2019). Among the most prominent approaches to ethics within philos ophy is a highly influential position known as consequentialism. Roughly speaking, the consequentialist believes that out comes are all that matter, and that people should therefore endeavour to act so as to produce the best consequences, based on an impart ial perspective as to what is best . Although there are numerous difficulties with consequentia lism in practice (see §4), it nevertheless provides a clear and principled foundation from which to critiq ue proposals which fall short of its ideals. In this paper, we analyze the literature on fairness within mac hine learning, and show how it largely depends on assumptions which the consequentialist perspective rev eals immediately to be problematic. In particular, we make the following contributions: - We provide an accessible overview of the main ideas of conseq uentialism ( §3), as well as a discussion of its difficulties ( §4), with a special emphasis on computational limitations. 1 - We review the dominant ideas about fairness in the machine le arning literature ( §5), and provide the first critique of these ideas explicitly from the perspectiv e of consequentialism ( §6). - We conclude with a broader discussion of the ethical issues r aised by learning and randomization, highlighting future direction for both AI and consequentia lism ( §7).

Artificial intelligence is increasingly playing a role in companies' hiring decisions. Algorithms help target ads about new positions, sort through resumes, and even analyze applicants' facial expressions during video job interviews. But these systems are opaque, and we often have no idea how artificial intelligence-based systems are sorting, scoring, and ranking our applications. It's not just that we don't know how these systems work. Artificial intelligence can also introduce bias and inaccuracy to the job application process, and because these algorithms largely operate in a black box, it's not really possible to hold a company that uses a problematic or unfair tool accountable.

It's easy to get cynical about artificial intelligence (AI). China is using facial recognition against the Uighurs. NYT: 'One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority' Google's participating in the development of autonomous weapons. The Intercept: 'Google Continues Investments in Military and Police AI Technology Through Venture Capital Arm' And facial recognition programmes are still struggling to recognise black faces. But last year I also saw another side.

At work, AI tells sales reps which accounts they should be pursuing and helps lawyers instantly analyze piles of contracts. Is it any wonder that we're starting to think it might be OK if the machines take over? A recent global survey found that 64 percent of more than 8,000 respondents said they didn't just embrace AI -- they would actually trust it more than their manager. Tony Deigh, chief technology officer at the Cambridge machine-learning-based employment platform Jobcase, understands this impulse. As AI gets better at recognizing complicated patterns from huge troves of data, it could conceivably be applied to many roles, like being a boss. "Would I take career advice from a machine?

Cyberlaw as a discipline saw some massive advances in 2019. These advances were seen in different thrust areas of this discipline. The first significant element of 2019 was the determined focus of sovereign governments across the world, to come up with strong national cybersecurity legislations and legislative frameworks. Consequently, different countries and sovereign governments started moving in the direction of trying to regulate cybersecurity. These regulations normally took two distinctive manifestations.

In a previous post, I expressed my happiness that I got to present at ML in PL in Warsaw. I had the opportunity to take a step back and reflect a bit on the ethics of what we do as practitioners of data science and builders of machine learning models. It's an important topic and doesn't receive the attention that it should. The algorithms we build affect lives. I have researched this topic quite a lot, and during that time I have found a number of stories that made a huge impression on me.

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Machine Learning and Artificial Intelligence are considered an integral part of the Fourth Industrial Revolution. Their impact, and far-reaching consequences, while acknowledged, are yet to be comprehended. These technologies are very specialized, and few organizations and select highly trained professionals have the wherewithal, in terms of money, manpower, and might, to chart the future. However, concentration of power can lead to marginalization, causing severe inequalities. Regulatory agencies and governments across the globe are creating national policies, and laws around these technologies to protect the rights of the digital citizens, as well as to empower them. Even private, not-for-profit organizations are also contributing to democratizing the technologies by making them \emph{accessible} and \emph{affordable}. However, accessibility and affordability are all but a few of the facets of democratizing the field. Others include, but not limited to, \emph{portability}, \emph{explainability}, \emph{credibility}, \emph{fairness}, among others. As one can imagine, democratizing AI is a multi-faceted problem, and it requires advancements in science, technology and policy. At \texttt{mlsquare}, we are developing scientific tools in this space. Specifically, we introduce an opinionated, extensible, \texttt{Python} framework that provides a single point of interface to a variety of solutions in each of the categories mentioned above. We present the design details, APIs of the framework, reference implementations, road map for development, and guidelines for contributions.