If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Perhaps you remember the iconic theme of the globally popular Kung Fu Panda movies, "You are the secret ingredient!" This meant that self-belief is important and with it great things can be achieved--Po, for example, became the Dragon Warrior. My meaning here is that computer science is both a powerful enabler of rapid advances in all intellectual fields and a disruptor driving furious revolutions in commerce and society worldwide. Computer science is more important and potent than ever! Computing is driving unprecedented rapid change.
It is fall in Heidelberg and the leaves on the trees are already turning. This is the fifth year of the Heidelberg Laureate Forum (http://www.heidelberg-laureate-forum.org/) and it continues to be a highlight of the year for me and for about 250 others who participate. This year, computer science was heavily represented. There were fewer mathematicians, but they made up for smaller numbers by their extraordinary qualifications. A new cohort of laureates was added this year: recipients of the ACM Prize for Computing.a
The network, through iterated adjustment of the elements of the vector based on errors detected on comparison with the text corpora, produces the values in continuous space that best reflect the contextual data given. Most dictionaries will offer a direct or indirect connection through "king" to "ruler" or "sovereign" and "male" and through "queen" to "ruler" or "sovereign" and "female," as: These definitions2 show gender can be "factored out," and in common usage the gender aspect of sovereigns is notable. As we understand the high degree of contextual dependency of word meanings in a language, any representation of word meaning to a significant degree will reflect context, where context is its interassociation with other words. The word vectors produced by the method of training on a huge natural text dataset, in which words are given distributed vector representations refined through associations present in the input context, reflect the cross-referential semantic compositionality of a dictionary.
The compounding of this continued and accelerating advance give rise to a deep technical expertise. While deep technical challenges abound, the ethical challenges, principles, and standards are even more daunting. Second, societies develop and advocate principles for ethical technical conduct that frame the role of computing professionals, and buttress them with the stature and role of the profession in society. Necessarily so, as technical knowledge and professional ethics must inform professional conduct, and inevitably come into conflict with personal interest, corporate interest, government or national interest, or even overt coercion.
On the other hand, some HPC systems run highly exotic hardware and software stacks. This fact means that aside from all of the normal reasons that any network-connected computer might be attacked, HPC computers have their own distinct systems, resources, and assets that an attacker might target, as well as their own distinctive attributes that make securing such systems somewhat distinct from securing other types of computing systems. As a result, although I discuss confidentiality, a typical component of the "C-I-A" triad, because even in open science, data leakage is certainly an issue and a threat, this article focuses more on integrity related threats,31,32 including alteration of code or data, or misuse of computing cycles, and availability related threats, including disruption or denial of service against HPC systems or networks that connect them. The diagram at top shows a typical workflow for data analysis in HPC; the middle diagram shows a typical workflow for modeling and simulation; and the bottom diagram shows a coupled, interactive compute-visualization workflow.
Among the 22 Turing Laureates in attendance at the conference were: Front row, from left: Whitfield Diffie (2015), Martin Hellman (2015), Robert Tarjan (1986), Barbara Liskov (2008). Among the 22 Turing Laureates in attendance at the conference were: Front row, from left: Whitfield Diffie (2015), Martin Hellman (2015), Robert Tarjan (1986), Barbara Liskov (2008). Butler Lampson, the 1992 Turing Laureate ("for contributions to the development of distributed, personal computing environments and the technology for their implementation: workstations, networks, operating systems, programming systems, displays, security, and document publishing"), said, "There's plenty of room at the top; there's room in software, algorithms, and hardware." A panel on Moore's Law was moderated by John Hennessy (left) and included Doug Burger, Norman Jouppi, Butler Lampson (1992), and Margaret Martonosi.
The ACM U.S. Public Policy Council (USACM) was established in the early 1990s as a focal point for ACM's interactions with U.S. government organizations, the computing community, and the public in all matters of U.S. public policy related to information technology. USACM and EUACM have identified and codified a set of principles intended to ensure fairness in this evolving policy and technology ecosystem.a These are: (1) awareness; (2) access and redress; (3) accountability; (4) explanation; (5) data provenance; (6) audit-ability; and (7) validation and testing. As organizations deploy complex algorithms for automated decision making, system designers should build these principles into their systems. USACM and EUACM seek input and involvement from ACM's members in providing technical expertise to decision makers on the often difficult policy questions relating to algorithmic transparency and accountability, as well as those relating to security, privacy, accessibility, intellectual property, big data, voting, and other technical areas.
I treat data science problems as complex systems involving comprehensive system complexities, or X-complexities, in terms of data (characteristics), behavior, domain, social factors, environment (context), learning (process and system), and deliverables. Data complexity is reflected in terms of sophisticated data circumstances and characteristics, including large scale, high dimensionality, extreme imbalance, online and real-time interaction and processing, cross-media applications, mixed sources, strong dynamics, high frequency, uncertainty, noise mixed with data, unclear structures, unclear hierarchy, heterogeneous or unclear distribution, strong sparsity, and unclear availability of specific sometimes critical data. It may be embodied in such aspects of business problems as social networking, community emergence, social dynamics, impact evolution, social conventions, social contexts, social cognition, social intelligence, social media, group formation and evolution, group interaction and collaboration, economic and cultural factors, social norms, emotion, sentiment and opinion influence processes, and social issues, including security, privacy, trust, risk, and accountability in social contexts. Environment complexity is another important factor in understanding complex data and business problems, as reflected in environmental (contextual) factors, contexts of problems and data, context dynamics, adaptive engagement of contexts, complex contextual interactions between the business environment and data systems, significant changes in business environment and their effect on data systems, and variations and uncertainty in interactions between business data and the business environment.