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.
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
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,