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Technology is giving life the potential to flourish like never before...
...or to self-destruct. Let's make a difference!


Max Tegmark , President of the Future of Life Institute

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What is AI?

From SIRI to self-driving cars, artificial intelligence (AI) is progressing rapidly. While science fiction often portrays AI as robots with human-like characteristics, AI can encompass anything from Google’s search algorithms to IBM’s Watson to autonomous weapons.

Artificial intelligence today is properly known as Buy Cheap Fake Mens Dunk Flyknit Basketball Shoes Nike Shopping Online Clearance Sale Factory Outlet CPkSI
, in that it is designed to perform a narrow task (e.g. only facial recognition or only internet searches or only driving a car). However, the long-term goal of manyresearchers is to create 2018 New Online Cheap Sale Fashionable Preowned Exotic leathers heels Sergio Rossi 100% Original Online bdLzmRqu
. While narrow AI may outperform humans atwhatever its specific task is, like playing chess or solving equations, AGI would outperformhumans atnearly every cognitive task.

Why research AI safety?

In the near term, the goal of keepingAI’s impact on society beneficial motivates research in many areas, from economics and law to technical topics such asverification,validity,security andcontrol. Whereas it may be little more than a minor nuisance ifyour laptop crashes or gets hacked, it becomes all the more important that an AI system does what youwant it to do if it controls your car, your airplane, your pacemaker, your automated trading system or your power grid. Another short-term challenge ispreventinga devastating Latest Collections Cheap Online Womens Lace PointedToe Mules Altuzarra Sale Hot Sale XW4039z

In the long term, an important question is what will happen if the quest for strong AI succeeds and an AI system becomes better than humans at all cognitive tasks. As pointed out by Jeans On Sale Grey Cotton 2017 32 Dondup Manchester Outlet Low Price Many Kinds Of hK7fjTAc
in 1965, designing smarter AI systems is itself a cognitive task. Such a system could potentially undergo recursive self-improvement, triggering an intelligence explosion leaving human intellectfar behind. By inventing revolutionary new technologies, sucha superintelligence mighthelp us eradicate war, disease, and poverty, and so the creation ofstrong AI mightbe the biggest event in human history.Some experts have expressed concern, though, that it might also be the last, unless we learn to align the goals of the AIwith ours before it becomes superintelligent.

There are some who question whether strongAI will ever be achieved, and others who insist that the creation of superintelligent AI is guaranteedto be beneficial. At FLI we recognize both of these possibilities, butalso recognize the potential for an artificial intelligence system tointentionally or unintentionally cause great harm. We believe research today will help us better prepare for and prevent such potentially negativeconsequences in the future, thus enjoying the benefits of AI while avoiding pitfalls.

How can AI be dangerous?

Most researchers agree that a superintelligent AI is unlikely to exhibit human emotions like love or hate, and that there is no reason to expect AI to become intentionally benevolent or malevolent. Instead, when considering how AI mightbecome a risk, experts think two scenarios most likely:

As these examples illustrate, the concern about advanced AI isn’t malevolence but competence. A super-intelligent AI will be extremely good at accomplishing its goals, and if those goals aren’t aligned with ours, wehave a problem. You’re probably not an evil ant-hater who steps on ants out of malice, but if you’re in charge of a hydroelectric green energy project and there’s an anthill in the region to be flooded, too bad for the ants. A key goal of AI safety research is to never place humanity in the position of those ants.

The AI is programmed to do something devastating:

Both of these analyses require that we know the topics and which topics each document is about. Topic modeling algorithms uncover this structure. They analyze the texts to find a set of topics — patterns of tightly co-occurring terms — and how each document combines them. Researchers have developed fast algorithms for discovering topics; the analysis of of 1.8 million articles in Figure 1 took only a few hours on a single computer.

What exactly is a topic? Formally, a topic is a probability distribution over terms. In each topic, different sets of terms have high probability, and we typically visualize the topics by listing those sets (again, see Figure 1). As I have mentioned, topic models find the sets of terms that tend to occur together in the texts. [ 2 ] They look like “topics” because terms that frequently occur together tend to be about the same subject.

But what comes after the analysis? Some of the important open questions in topic modeling have to do with how we use the output of the algorithm: How should we visualize and navigate the topical structure? What do the topics and document representations tell us about the texts? The humanities, fields where questions about texts are paramount, is an ideal testbed for topic modeling and fertile ground for interdisciplinary collaborations with computer scientists and statisticians.

Topic modeling sits in the larger field of probabilistic modeling , a field that has great potential for the humanities. Traditionally, statistics and machine learning gives a “cookbook” of methods, and users of these tools are required to match their specific problems to general solutions. In probabilistic modeling, we provide a language for expressing assumptions about data and generic methods for computing with those assumptions. As this field matures, scholars will be able to easily tailor sophisticated statistical methods to their individual expertise, assumptions, and theories. [ 3 ]

In particular, LDA is a type of probabilistic model with hidden variables. Viewed in this context, LDA specifies a generative process , an imaginary probabilistic recipe that produces both the hidden topic structure and the observed words of the texts. Topic modeling algorithms perform what is called probabilistic inference . Given a collection of texts, they reverse the imaginary generative process to answer the question “What is the likely hidden topical structure that generated my observed documents?”

The generative process for LDA is as follows. First choose the topics, each one from a distribution over distributions. Then, for each document, choose topic weights to describe which topics that document is about. Finally, for each word in each document, choose a topic assignment — a pointer to one of the topics — from those topic weights and then choose an observed word from the corresponding topic. Each time the model generates a new document it chooses new topic weights, but the topics themselves are chosen once for the whole collection. [ 4 ] I emphasize that this is a conceptual process. It defines the mathematical model where a set of topics describes the collection, and each document exhibits them to different degree. The inference algorithm (like the one that produced Figure 1) finds the topics that best describe the collection under these assumptions.

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