The article is providing an answer to the following questions that are relevant to the study of intelligence:
What is “understanding”?
How can we tell whether a system has it or not? https://bit.ly/3fynqYu
#AI #MechineLearning #neuroscience #consciousness #philosophy #technology #cognition #intelligence
NASA goes old school in its concept study for a robotic surface hopper designed to explore the foreboding surfaces of Europa and Enceladus.
A collective of more than 1,000 researchers, academics and experts in artificial intelligence are speaking out against soon-to-be-published research that claims to use neural networks to “predict criminality.” At the time of writing, more than 50 employees working on AI at companies like Facebook, Google and Microsoft had signed on to an open letter opposing the research and imploring its publisher to reconsider.
The controversial research is set to be highlighted in an upcoming book series by Springer, the publisher of Nature. Its authors make the alarming claim that their automated facial recognition software can predict if a person will become a criminal, citing the utility of such work in law enforcement applications for predictive policing.
“By automating the identification of potential threats without bias, our aim is to produce tools for crime prevention, law enforcement, and military applications that are less impacted by implicit biases and emotional responses,” Harrisburg University professor and co-author Nathaniel J.S. Ashby said.
A startup recently emerging from stealth is aiming to automate much of the process for feature development with the goal of making data scientists more self-reliant.
Circa 2010
Updated at 18:30 EST to correct timeline of prediction to 2030 from 2020 Reverse-engineering the human brain so we can simulate it using computers may be just two decades away, says Ray Kurzweil, artificial intelligence expert and author of the best-selling book The Singularity is Near. It would be the first step toward creating machines \[…\].
Genetic perturbations that affect bacterial resistance to antibiotics have been characterized genome-wide, but how do such perturbations interact with subsequent evolutionary adaptation to the drug? Here, we show that strong epistasis between resistance mutations and systematically identified genes can be exploited to control spontaneous resistance evolution. We evolved hundreds of Escherichia coli K-12 mutant populations in parallel, using a robotic platform that tightly controls population size and selection pressure. We find a global diminishing-returns epistasis pattern: strains that are initially more sensitive generally undergo larger resistance gains. However, some gene deletion strains deviate from this general trend and curtail the evolvability of resistance, including deletions of genes for membrane transport, LPS biosynthesis, and chaperones. Deletions of efflux pump genes force evolution on inferior mutational paths, not explored in the wild type, and some of these essentially block resistance evolution. This effect is due to strong negative epistasis with resistance mutations. The identified genes and cellular functions provide potential targets for development of adjuvants that may block spontaneous resistance evolution when combined with antibiotics.
Not even the lawyers will be spared.
In the summer of 2015, Stanford-bound high school grad Josh Browder spent his nights coding and developing an automated program that would help people contest parking tickets. The native Londoner had recently gotten his driver’s license, and had himself assembled a respectable collection of fines, some of which he felt were unjustly rewarded.
About three weeks later, Browder already had a product called DoNotPay which he shared with his friends. A blogger from Reddit picked up on it, and almost overnight, DoNotPay went from 10 people using it to 50,000 users.
Today, the company announced it closed a $12 million Series A at an $80 million valuation. Coatue led the round, with participation from Andreessen Horowitz, Founders Fund and Felicis Ventures. All had previously invested in the company’s $4.6 million seed round.
A.i assisting the doctors.
Artificial Intelligence (AI) is increasingly being used in medicine to support human expertise. However, the potential of these applications and the risks inherent in the interaction between human and artificial intelligence have not yet been thoroughly researched. The fear is often expressed that in future, as soon as AI is of sufficient quality, human expertise will become dispensable and therefore fewer doctors will be needed. These fears are further fuelled by the popular portrayal of this as a “competition” between humans and AI. An international study led by MedUni Vienna has now illustrated the enormous potential of human/computer collaboration.
The international study led by Philipp Tschandl and Harald Kittler (Department of Dermatology, MedUni Vienna) and Christoph Rinner (CeMSIIS/Institute for Medical Information Management, MedUni Vienna) now debunks the idea of this alleged competition, highlighting instead the enormous potential of combining human expertise with Artificial Intelligence. The study published in Nature Medicine examines the interaction between doctors and AI from various perspectives and in different scenarios of practical relevance. Although the authors restrict their observations to the diagnosis of skin cancers, they stress that the findings can also be extrapolated to other areas of medicine where Artificial Intelligence is used.
AI does not always improve diagnosis
In an experiment created by the study authors, 302 examiners and/or doctors had to assess dermoscopic images of benign and malignant skin changes, both with and without the support of Artificial Intelligence. The AI assessment was provided in three different variants. In the first case, AI showed the examiner the probabilities of all possible diagnoses, in the second case the probability of a malignant change and, in the third case, a selection of similar images with known diagnoses, similar to a Google image search. As a main finding the authors observed that only in the first case did collaboration with AI improve the examiners’ diagnostic accuracy, although this was significant, with a 13% increase in correct diagnoses.
