"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
"This is a key first step in being able to shed light on serial hijackers' behavior," says MIT Ph.D. candidate Cecilia Testart. Hijacking IP addresses is an increasingly popular form of cyber-attack. This is done for a range of reasons, from sending spam and malware to stealing Bitcoin. It's estimated that in 2017 alone, routing incidents such as IP hijacks affected more than 10 percent of all the world's routing domains. There have been major incidents at Amazon and Google and even in nation-states -- a study last year suggested that a Chinese telecom company used the approach to gather intelligence on western countries by rerouting their Internet traffic through China.
A self-driving car approaches a stop sign, but instead of slowing down, it accelerates into the busy intersection. An accident report later reveals that four small rectangles had been stuck to the face of the sign. These fooled the car's onboard artificial intelligence (AI) into misreading the word'stop' as'speed limit 45'. Such an event hasn't actually happened, but the potential for sabotaging AI is very real. Researchers have already demonstrated how to fool an AI system into misreading a stop sign, by carefully positioning stickers on it1. They have deceived facial-recognition systems by sticking a printed pattern on glasses or hats. And they have tricked speech-recognition systems into hearing phantom phrases by inserting patterns of white noise in the audio.
When data scientists in Chicago, Illinois, set out to test whether a machine-learning algorithm could predict how long people would stay in hospital, they thought that they were doing everyone a favour. Keeping people in hospital is expensive, and if managers knew which patients were most likely to be eligible for discharge, they could move them to the top of doctors' priority lists to avoid unnecessary delays. It would be a win–win situation: the hospital would save money and people could leave as soon as possible. Starting their work at the end of 2017, the scientists trained their algorithm on patient data from the University of Chicago academic hospital system. Taking data from the previous three years, they crunched the numbers to see what combination of factors best predicted length of stay.
Agbiotech newcomer Inari has raised $89 million to pursue an ambitious goal: to challenge the status quo in agriculture. Plants edited with the new genome editing tools will incorporate useful traits and will not be classed as GMOs.Credit: reHAWKEYE / Alamy Stock Photo Inari is one of a several small companies with similarly lofty goals who are capitalizing on new editing technologies, such as CRISPR, and computational methods for predictive modeling. Such tools make crop development faster and less expensive, and potentially could give startups a shot at competing with the big players by sidestepping onerous and expensive regulatory oversight. Just a few years ago, a seed developer could plan on spending a decade and up to $100 million on bringing one new crop trait to market (Nat. That's not only because the old tools for altering the genetics of these crops, such as Agrobacterium-mediated transformation, were slower, more expensive and more unpredictable than CRISPR, but also because of regulations, both in the United States and especially in Europe.
It is possible to train just a neural network to answer questions about a scene by feeding in millions of examples as training data. But a human child doesn't require such a vast amount of data in order to grasp what a new object is or how it relates to other objects. Also, a network trained that way has no real understanding of the concepts involved--it's just a vast pattern-matching exercise. So such a system would be prone to making very silly mistakes when faced with new scenarios. This is a common problem with today's neural networks and underpins shortcomings that are easily exposed (see "AI's language problem").
Last year the United States Food and Drug Administration (FDA) cleared a total of 12 AI tools that use machine learning for health (ML4H) algorithms to inform medical diagnosis and treatment for patients. The tools are now allowed to be marketed, with millions of potential users in the US alone.Because ML4H tools directly affect human health, their development from experiments in labs to deployment in hospitals progresses under heavy scrutiny. A critical component of this process is reproducibility. A team of researchers from MIT, University of Toronto, New York University, and Evidation Health have proposed a number of "recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward" in their new paper Reproducibility in Machine Learning for Health. Just as boxers show their strength in the ring by getting up again after being knocked to the canvas, researchers test their strength in the arena of science by ensuring their work's reproducibility.
Despite 60% of Marketers Demanding Control of the'Digital Experience', Many Do Not Understand Common Digital Terms Despite 60% of marketers wanting to'own' the digital experience, many admit that they don't fully understand digital terminology such as API, big data and machine learning. The research, which surveyed over 200 IT professionals and 200 marketers, explores the growing disconnect between each group as they struggle to decide who should'own' the emerging digital experience sector. Magnolia found that 24% of marketers don't understand what'machine learning' is, and 23% say they don't know what the term'big data' means. A third of marketers also confess to not know what API stands for. IT teams are also suffering from a similar disconnect, with 77% saying they don't understand the buzzwords marketers use.
Over the last decade, companies have begun to grasp and unlock the potential that artificial intelligence (AI) and machine learning (ML) can bring. While still in its infancy, companies are starting to understand the significant impact this technology can bring, helping them make better, faster and more efficient decisions. Of course, AI and ML is no silver bullet to help businesses embrace innovation. In fact, the success of these algorithms is only as good as their foundations -- specifically, quality data. Without it, businesses will see the very objective they've installed AI and ML to do fail, with the unforeseen consequences of bad data causing irreversible damage to the business both in terms of its efficiency and reputation.
The complete part of the earthquake frequency–magnitude distribution, above the completeness magnitude mc, is well described by the Gutenberg–Richter law. On the other hand, incomplete data does not follow any specific law, since the shape of the frequency–magnitude distribution below max(mc) is function of mc heterogeneities that depend on the seismic network spatiotemporal configuration. This paper attempts to solve this problem by presenting an asymmetric Laplace mixture model, defined as the weighted sum of Laplace (or double exponential) distribution components of constant mc, where the inverse scale parameter of the exponential function is the detection parameter κ below mc, and the Gutenberg–Richter β-value above mc. Using a variant of the Expectation-Maximization algorithm, the mixture model confirms the ontology proposed by Mignan [2012, https://doi.org/10.1029/2012JB009347], The performance of the proposed mixture model is analysed, with encouraging results obtained in simulations and in eight real earthquake catalogues that represent different seismic network spatial configurations.
Today, a fresh generation of technologies, fuelled by advances in artificial intelligence based on machine learning, is opening up new opportunities to reassess the upper bounds of operational excellence across these sectors. To stay one step ahead of the pack, businesses not only need to understand machine learning complexities but be prepared to act on it and take advantage. After all, the latest machine learning solutions can determine weeks in advance if and when assets are likely to degrade or fail, distinguishing between normal and abnormal equipment and process behaviour by recognising complex data patterns and uncovering the precise signatures of degradation and failure. They can then alert operators and even prescribe solutions to avoid the impending failure, or at least mitigate the consequences. The leading software constructs are autonomous and self-learning.