Author: cxdig

The WIRED Guide to Artificial Intelligence

Supersmart algorithms won’t take all the jobs, But they are learning faster than ever, doing everything from medical diagnostics to serving up ads.

 

Even if progress on making artificial intelligence smarter stops tomorrow, don’t expect to stop hearing about how it’s changing the world. Big tech companies such as Google, Microsoft, and Amazon have amassed strong rosters of AI talent and impressive arrays of computers to bolster their core businesses of targeting ads or anticipating your next purchase.

 

They’ve also begun trying to make money by inviting others to run AI projects on their networks, which will help propel advances in areas such as health care or national security. Improvements to AI hardware, growth in training courses in machine learning, and open source machine-learning projects will also accelerate the spread of AI into other industries.

Artificial general intelligence

As yet nonexistent software that displays a humanlike ability to adapt to different environments and tasks, and transfer knowledge between them. Meanwhile, consumers can expect to be pitched more gadgets and services with AI-powered features. Google and Amazon in particular are betting that improvements in machine learning will make their virtual assistants and smart speakers more powerful. Amazon, for example, has devices with cameras to look at their ownersand the world around them.

 

The commercial possibilities make this a great time to be an AI researcher. Labs investigating how to make smarter machines are more numerous and better-funded than ever. And there’s plenty to work on: Despite the flurry of recent progress in AI and wild prognostications about its near future, there are still many things that machines can’t do, such as understanding the nuances of language, common-sense reasoning, and learning a new skill from just one or two examples. AI software will need to master tasks like these if it is to get close to the multifaceted, adaptable, and creative intelligence of humans. One deep-learning pioneer, Google’s Geoff Hinton, argues that making progress on that grand challenge will require rethinking some of the foundations of the field.

 

As AI systems grow more powerful, they will rightly invite more scrutiny. Government use of software in areas such as criminal justice is often flawed or secretive, and corporations like Facebook have begun confronting the downsides of their own life-shaping algorithms. More powerful AI has the potential to create worse problems, for example by perpetuating historical biases and stereotypes against women or black people. Civil-society groups and even the tech industry itself are now exploring rules and guidelines on the safety and ethics of AI. For us to truly reap the benefits of machines getting smarter, we’ll need to get smarter about machines.

Source: www.wired.com

NECSI Winter School

Winter Session 2020

Gain new insights that reframe your thinking, specific tools to advance current projects, and perspectives to set new directions.

Dates: January 6 – 17

Location: MIT, Cambridge, MA
The NECSI Winter School offers two intensive week-long courses on complexity science: modeling and networks, and data analytics. You may register for any of the weeks. If desired, arrangements for credit at a home institution may be made in advance.

  • Week 1: January 6-10 CX201: Complex Physical, Biological and Social Systems
  • Week 2: January 12-17 CX202: Complex Systems Modeling and Networks

Source: necsi.edu

Training-free measures based on algorithmic probability identify high nucleosome occupancy in DNA sequences

We introduce and study a set of training-free methods of an information-theoretic and algorithmic complexity nature that we apply to DNA sequences to identify their potential to identify nucleosomal binding sites. We test the measures on well-studied genomic sequences of different sizes drawn from different sources. The measures reveal the known in vivo versus in vitro predictive discrepancies and uncover their potential to pinpoint high and low nucleosome occupancy. We explore different possible signals within and beyond the nucleosome length and find that the complexity indices are informative of nucleosome occupancy. We found that, while it is clear that the gold standard Kaplan model is driven by GC content (by design) and by k-mer training; for high occupancy, entropy and complexity-based scores are also informative and can complement the Kaplan model.

 

Training-free measures based on algorithmic probability identify high nucleosome occupancy in DNA sequences
Hector Zenil, Peter Minary
Nucleic Acids Research, gkz750,

Source: academic.oup.com

Bilateral relatedness: knowledge diffusion and the evolution of bilateral trade

During the last two decades, two important contributions have reshaped our understanding of international trade. First, countries trade more with those with whom they share history, language, and culture, suggesting that trade is limited by information frictions. Second, countries are more likely to start exporting products that are related to their current exports, suggesting that shared capabilities and knowledge diffusion constrain export diversification. Here, we join both of these streams of literature by developing three measures of bilateral relatedness and using them to ask whether the destinations to which a country will increase its exports of a product are predicted by these forms of relatedness. The first form is product relatedness, and asks whether a country already exports many similar products to a destination. The second is importer relatedness, and asks whether the country exports the same product to the neighbors of the target destination. The third is exporter relatedness, and asks whether a country’s neighbors are already exporting the same product to the destination. We use bilateral trade data from 2000 to 2015, and a variety of controls in multiple gravity specifications, to show that countries are more likely to increase their exports of a product to a destination when they have more product relatedness, importer relatedness, and exporter relatedness. Then, we use several sample splits to explore whether the effects of these forms of relatedness are stronger for products of higher complexity, technological sophistication, and differentiation. We find that, in the case of product relatedness, the effects are stronger for differentiated, complex, and technologically sophisticated products. Also, we find the effects of common language and shared colonial past to increase with differentiation, complexity, and technological sophistication, while the effects of shared borders decrease with these three variables. These results suggest that product relatedness and common language capture dimensions of knowledge relatedness that are more important for the exchange of more sophisticated and differentiated products. These findings extend the ideas of relatedness to bilateral trade and show that the evolution of bilateral trade networks are shaped by relatedness among products, exporters, and importers.

 

Bilateral relatedness: knowledge diffusion and the evolution of bilateral trade
Bogang Jun, Aamena Alshamsi, Jian Gao, César A. Hidalgo

Journal of Evolutionary Economics

https://t.co/6MG2YPWeM7?amp=1

Source: link.springer.com

Innovation and The Evolution of the Economic Web

Fifty thousand years ago the global economy may have had a diversity of a few thousand goods and services, including fire, unifacial stone scrapers, hides, and so forth. Today, in New York alone, there must be over a billion goods and services. The global economy has exploded in diversity. The question is how and why has this explosion occurred?
The economy, as detailed a bit further below, is a network of complements and substitutes, which I will call the Economic Web. And like the biosphere, it’s evolution is substantially unprestatable, “context dependent,” and creates its own growing “context” that comprises its “Adjacent Possible.” The adjacent possible is what can arise next in this evolution. This evolution is “sucked into” the very opportunities it itself creates. Innovations into the Adjacent Possible drive this growth.
I do not wish to consider here the rich evolution of a single technology. Brian Arthur has brilliantly done so in his book The Nature of Technology [1]. Rather, I wish to discuss the evolution of the entire economic web, for as we shall see, goods and services create novel niches which invite the innovative creation of new complementary and substitute goods such that the web as a whole grows in diversity.

 

Innovation and The Evolution of the Economic Web
by Stuart Kauffman

Entropy 2019, 21(9), 864

Source: www.mdpi.com