Assistant HHS Secretary Admiral Brett Giroir weighs in on the coronavirus pandemic on'The Daily Briefing.' The leaders of the House Problem Solvers Caucus Friday expressed optimism that Republicans and Democrats will soon come together on a major coronavirus deal to continue supplemental unemployment benefits, help struggling small businesses and fund the reopening of schools. Tom Reed, R-N.Y., and Josh Gottheimer, D-N.J., predict an agreement will come within a matter of days. Negotiators are under pressure to act due to Friday's expiration of $600-per-week federal unemployment benefits, schools needing help to reopen this month and lawmakers wanting to preserve their August recess. "I think we're going to get this done this coming week," Gottheimer said in an interview with Fox News on Friday.
Speedcubing is the sport of solving a classic Rubik's Cube -- or a related combination puzzle -- in the shortest amount of time possible. And, no, it is not for the faint of heart. The new Netflix documentary on this subject, The Speed Cubers, dives headfirst into the friendly but competitive speedcubing culture. The 40-minute film is one of three new documentary shorts debuting on Netflix this summer. The Speed Cubers centers on a couple of professional competitors who go head-to-head at the World Cube Association World Championship in Melbourne, Australia, in 2019.
What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
Artificial intelligence has made significant strides in recent years, but modern AI techniques remain limited, a panel of MIT professors and the director of the MIT-IBM Watson AI Lab said during a webinar this week. Neural networks can perform specific, well-defined tasks but they struggle in real-world situations that go beyond pattern recognition and present obstacles like limited data, reliance on self-training, and answering questions like "why" and "how" versus "what," the panel said. The future of AI depends on enabling AI systems to do something once considered impossible: Learn by demonstrating flexibility, some semblance of reasoning, and/or by transferring knowledge from one set of tasks to another, the group said. The panel discussion was moderated by David Schubmehl, a research director at IDC, and it began with a question he posed asking about the current limitations of AI and machine learning. "The striking success right now in particular, in machine learning, is in problems that require interpretation of signals--images, speech and language," said panelist Leslie Kaelbling, a computer science and engineering professor at MIT.
This is just meant as a friendly introduction to a topic that every computer science and data science program I know off explores in an entire course or a few. Working with any kind of algorithm starts with learning a set of data structures associated with it. This makes sense since most algorithms work on some kind of data that must be stored and held somehow, somewhere. That's where data structures come handy! Data Structures are used to organize information and data in a variety of ways such that an algorithm can be applied to the structure in the most efficient way possible.
The creators of the Python language are mulling a new proposal, PEP 622, that would finally bring a pattern matching statement syntax to Python. The new pattern matching statements would give Python programmers more expressive ways of handling structured data, without having to resort to workarounds. Pattern matching is a common feature of many programming languages, such as switch/case in C. It allows one of a number of possible actions to be taken based on the value of a given variable or expression. While Python has lacked a native syntax for pattern matching, it has been possible to emulate it with if/elif/else chains or a dictionary lookup. Supported pattern match types include literals, names, constant values, sequences, a mapping (basically, the presence of a key-value pair in the expression), a class, a mixture of the above, or any of those plus conditional expressions.
A major strength of frame-based knowledge representation languages is their ability to provide the knowledge base designer with a concise and intuitively appealing means expression. The claim of intuitive appeal is based on the observation that the object -centered style of description provided by these languages often closely matches a designer's understanding of the domain being modeled and therefore lessens the burden of reformulation involved in developing a formal description. To be effective as a knowledge base development tool, a language needs to be supported by an implementation that facilitates creating, browsing, debugging, and editing the descriptions in the knowledge base. We have focused on providing such support in a SmallTalk (Ingalls, 1978) implementation of the KL-ONE knowledge representation language (Brachman, 1978), called KloneTalk, that has been in use by several projects for over a year at Xerox PARC. In this note, we describe those features of KloneTalk's displaybased interface that have made it an effective knowledge base development tool, including the use of constraints to automatically determine descriptions of newly created data base items.
Our group's work in medical decision making has led us to formulate a framework for expert system design, in particular about how the domain knowledge may be decomposed into substructures. We propose that there exist different problem-solving types, i.e., uses of knowledge, and corresponding to each is a separate substructure specializing in that type of problem-solving. Each substructure is in turn further decomposed into a hierarchy of specialist which differ from each other not in the type of problem-solving, but in the conceptual content of their knowledge; e.g.; one of them may specialize in "heart disease," while another may do so in "liver," though both of them are doing the same type of problem solving. Thus ultimately all the knowledge in the system is distributed among problem-solvers which know how to use that knowledge. This is in contrast to the currently dominant expert system paradigm which proposes a common knowledge base accessed by knowledge-free problem-solvers of various kinds. In our framework there is no distinction between knowledge bases and problem-solvers: each knowledge source is a problem-solver.