artificialintelligence   6407

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Artificial Intelligence | ScienceSquared
Sure, computers can do sums. But can they think like a mathematician? In Prague Josef Urban wants to make computers prove theorems
TheoremProvers  artificialintelligence 
18 hours ago by researchknowledge
[1609.03543] Logical Induction
We present a computable algorithm that assigns probabilities to every logical statement in a given formal language, and refines those probabilities over time. For instance, if the language is Peano arithmetic, it assigns probabilities to all arithmetical statements, including claims about the twin prime conjecture, the outputs of long-running computations, and its own probabilities. We show that our algorithm, an instance of what we call a logical inductor, satisfies a number of intuitive desiderata, including: (1) it learns to predict patterns of truth and falsehood in logical statements, often long before having the resources to evaluate the statements, so long as the patterns can be written down in polynomial time; (2) it learns to use appropriate statistical summaries to predict sequences of statements whose truth values appear pseudorandom; and (3) it learns to have accurate beliefs about its own current beliefs, in a manner that avoids the standard paradoxes of self-reference.
19 hours ago by researchknowledge
T-800 (The Terminator) | Terminator Wiki | FANDOM powered by Wikia
Den berømte replik fra Terminator - "It can't be bargained with. It can't be reasoned with. It doesn't feel pity or remorse or fear and it absolutely will not stop EVER until you are dead!"
er faktisk meget god at have in mente, når det kommer til de rigtige kunstige intelligenser. For man kan virkelig ikke forhandle med dem, argumentere med dem, og de føler ingenting. De er maskiner - de har ikke nogen som helst af disse egenskaber. Og de stopper rent taktisk aldrig. Heller ikke når du er død.
aibog  quotes  artificialintelligence 
yesterday by
The seductive diversion of ‘solving’ bias in artificial intelligence • Medium
Julia Powles and Helen Nissenbaum:
<p>What has been remarkably underappreciated is the key interdependence of the twin stories of A.I. inevitability and A.I. bias. Against the corporate projection of an otherwise sunny horizon of unstoppable A.I. integration, recognizing and acknowledging bias can be seen as a strategic concession — one that subdues the scale of the challenge. Bias, like job losses and safety hazards, becomes part of the grand bargain of innovation.

The reality that bias is primarily a social problem and cannot be fully solved technically becomes a strength, rather than a weakness, for the inevitability narrative. It flips the script. It absorbs and regularizes the classification practices and underlying systems of inequality perpetuated by automation, allowing relative increases in “fairness” to be claimed as victories — even if all that is being done is to slice, dice, and redistribute the makeup of those negatively affected by actuarial decision-making.

In short, the preoccupation with narrow computational puzzles distracts us from the far more important issue of the colossal asymmetry between societal cost and private gain in the rollout of automated systems. It also denies us the possibility of asking: Should we be building these systems at all?

The endgame is always to “fix” A.I. systems, never to use a different system or no system at all.
In accepting the existing narratives about A.I., vast zones of contest and imagination are relinquished. What is achieved is resignation — the normalization of massive data capture, a one-way transfer to technology companies, and the application of automated, predictive solutions to each and every societal problem.

Given this broader political and economic context, it should not surprise us that many prominent voices sounding the alarm on bias do so with blessing and support from the likes of Facebook, Microsoft, Alphabet, Amazon, and Apple. These convenient critics spotlight important questions, but they also suck attention from longer-term challenges. The endgame is always to “fix” A.I. systems, never to use a different system or no system at all.</p>
artificialintelligence  machinelearning  bias 
yesterday by charlesarthur
DeepMind's AlphaZero algorithm taught itself to play Go, chess, and shogi with superhuman performance and then beat state-of-the-art programs specializing in each game. The ability of AlphaZero to adapt to various game rules is a notable step toward achie
We introduce a full evaluation of AlphaZero, published in the journal Science, which describes a single algorithm that taught itself from scratch how to master the games of chess, shogi (Japanese chess), and Go, convincingly beating a world champion program in each case. AlphaZero’s ability to learn each game by itself results in a distinctive, creative and dynamic playing style that has captured the attention of the chess community. The result also marks an important step towards creating a flexible, general-purpose system that could one day learn to solve many different important and complex scientific problems.
3 days ago by joeybaker
The Impact of Artificial Intelligence on the World Economy - CIO Journal. - WSJ
Over time, AI likely will become such a historical transformative technology. But other than a relatively small number of leading-edge firms, we’re still in the early stages of AI’s deployment.
artificialintelligence  wsj 
4 days ago by jorgebarba
Why will play a significant role every business's and user engagement going forwar…
ArtificialIntelligence  BigData  from twitter
4 days ago by jhill5
RT : NEW ISSUE IN FOCUS on : Political players between vision and regulation – where do their
AI  ArtificialIntelligence  from twitter
5 days ago by ckatzenbach

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