It's predatory, and a practice that's not only bad for consumers but doesn't even make sense as an actual product. Recently, Logitech's new chief executive Hanneke Faber spoke to The Verge's Decoder podcast, where Faber mentioned that she'd recently been shown an example of a "forever mouse." The idea, she said, was that you'd buy a well-made mouse, with great software and services that you'd constantly update, and never throw away. But then the other shoe dropped: "The business model obviously is the challenge there," she said. "So then software is even more important when you think about it. Can you come up with a service model?"

It was a weekday afternoon in the spring when my son's kindergarten teacher got in touch about the ghost teen. During a social-studies unit about families, the teacher reported, my son had regaled his classmates with tales of his eighteen-year-old brother, who picks him up every afternoon at dismissal. I laughed out loud when I received this note, which was sent via ClassDojo, the messaging app used by our public elementary school in Brooklyn. My son has no brother of any age, and yet I could picture this brother immediately--I imagined him, for some reason, as one of the seniors from "Dazed and Confused," leaning against his scuzzy, old Pontiac parked just outside the school gate, a Marlboro Red hanging from his lips, Foghat wafting from the tape deck. But the teacher did not seem amused.

Answer Set Programming with Quantifiers ASP(Q) extends Answer Set Programming (ASP) to allow for declarative and modular modeling of problems from the entire polynomial hierarchy. The first implementation of ASP(Q), called qasp, was based on a translation to Quantified Boolean Formulae (QBF) with the aim of exploiting the well-developed and mature QBF-solving technology. However, the implementation of the QBF encoding employed in qasp is very general and might produce formulas that are hard to evaluate for existing QBF solvers because of the large number of symbols and sub-clauses. In this paper, we present a new implementation that builds on the ideas of qasp and features both a more efficient encoding procedure and new optimized encodings of ASP(Q) programs in QBF. The new encodings produce smaller formulas (in terms of the number of quantifiers, variables, and clauses) and result in a more efficient evaluation process. An algorithm selection strategy automatically combines several QBF-solving back-ends to further increase performance. An experimental analysis, conducted on known benchmarks, shows that the new system outperforms qasp.

An expert on voice recognition and speech technologies responds to Ysabelle Cheung's "Galatea." When Joseph Faber invented the Euphonia, a mid-19th century analog voice synthesizer, people weren't impressed. They found Faber's invention to be a strange device with little to no purpose. In an attempt to create a machine that could mimic human speech, Faber was physically tethered to his invention, manipulating its bellows, gears, and hardware to produce human-like utterances--from short speeches to ghostly renditions of "God Save the Queen"--with a flat affect. One version of the machine was designed with a feminine face attached to its bellows, hair in ringlets and fair, smooth-looking skin.

We introduce the framework of qualitative optimization problems (or, simply, optimization problems) to represent preference theories. The formalism uses separate modules to describe the space of outcomes to be compared (the generator) and the preferences on outcomes (the selector). We consider two types of optimization problems. They differ in the way the generator, which we model by a propositional theory, is interpreted: by the standard propositional logic semantics, and by the equilibrium-model (answer-set) semantics. Under the latter interpretation of generators, optimization problems directly generalize answer-set optimization programs proposed previously. We study strong equivalence of optimization problems, which guarantees their interchangeability within any larger context. We characterize several versions of strong equivalence obtained by restricting the class of optimization problems that can be used as extensions and establish the complexity of associated reasoning tasks. Understanding strong equivalence is essential for modular representation of optimization problems and rewriting techniques to simplify them without changing their inherent properties.

From helping in optimising the yield of therapeutics to training staff for setting up large-scale manufacturing sites, cutting-edge technologies such as artificial intelligence (AI) and virtual reality (VR) can be used to fast track COVID-19 vaccine development worldwide, experts say. The search for a COVID-19 vaccine has expanded worldwide, with thousands of researchers collaborating at hundreds of laboratories to fight the virus that has infected 56 million people and claimed over 1.34 million lives so far. Recently, a panel of experts noted at the Berlin Science Week, a ten-day science festival, that AI and other technologies like machine learning (ML) can make sense of the mountains of data from several experiments by discovering patterns that a human brain might fail to spot. As vaccine candidates advance to the final phases of testing in humans, experts said AI would be vital for analysing clinical and immunological data rapidly. Rene Faber, from the pharmaceutical company Sartorius headquartered in Germany, said there is a need to utilise these "handy innovations."

Portuguese VC Faber has hit the first close of its Faber Tech II fund at €20.5 million ($24.3 million). The fund will focus on early-stage data-driven startups starting from Southern Europe and the Iberian peninsula, with the aim of reaching a final close of €30 million in the coming months. The fund is backed by European Investment Fund (EIF) and the local Financial Development Institution (IFD), with a joint commitment of €15 million (backed by the Investment Plan for Europe – the Juncker Plan and through the Portugal Tech program), alongside other private institutional and individual investors. Alexandre Barbosa, Faber's Managing Partner, said "The success of the first close of our new fund allows us to foresee a growth in the demand for this type of investment, as we believe digital transformation through Intelligence Artificial, Machine Learning and data science are increasingly relevant for companies and their businesses, and we think Southern Europe will be the launchpad of a growing number." Faber has already'warehoused' three initial investments.

We usually think of the term "technology" in very modern, even futuristic contexts. Yet the word has a long history, deriving from the Greek tekhnologia, meaning "science of craft" or "systematic treatment" of actions. These traits have been with us since humans first discovered tools. In fact, the investment-analyst profession emerged from ad hoc investment approaches, using systematic processes to analyze and evaluate the health and value of companies. Increasingly, those processes are being undertaken by what we usually mean when we say "technology": computer hardware and software. Methods of investment analysis and selection, as well as portfolio management, have been heavily influenced by "quants" for decades.

This story is part of a series on A.I. in healthcare. Onno Faber was a member of Silicon Valley's happy breed of tech startup founders when he was diagnosed with a rare genetic condition that can come with dire health damage, but few treatments. Faber responded with entrepreneurial zeal, exploring whether Silicon Valley's mastery of algorithms might help root out and defeat the threatening quirks in his genetic code. Without any ready-made solutions on hand from big drug companies and their established research teams, Faber started to recruit individuals to his cause. The results of Faber's crusade so far demonstrate a trait Silicon Valley has in common with living things--a startling talent for self-organization.

Boolean functions in Answer Set Programming have proven a useful modelling tool. They are usually specified by means of aggregates or external atoms. A crucial step in computing answer sets for logic programs containing Boolean functions is verifying whether partial interpretations satisfy a Boolean function for all possible values of its undefined atoms. In this paper, we develop a new methodology for showing when such checks can be done in deterministic polynomial time. This provides a unifying view on all currently known polynomial-time decidability results, and furthermore identifies promising new classes that go well beyond the state of the art. Our main technique consists of using an ordering on the atoms to significantly reduce the necessary number of model checks. For many standard aggregates, we show how this ordering can be automatically obtained.