If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Stanford researchers have developed a machine-learning algorithm that can diagnose pneumonia from a chest x-ray better than a human radiologist can. And it learned how to do so in just about a month. The Machine Learning Group, led by Stanford adjunct professor Andrew Ng, was inspired by a data set released by the National Institutes of Health on 26 September. The data set contains 112,120 chest X-ray images labeled with 14 different possible diagnoses, along with some preliminary algorithms. The researchers asked four Stanford radiologists to annotate 420 of the images for possible indications of pneumonia.
Weather can cause significant fluctuations in consumer demand, and because of the bullwhip effect, it can produce unnecessarily high fluctuations on the supply side as well. And these types of variations typically turn into costs. Prepare too extensively, and you'll end up breaching the capacity limitations at every level of your supply chain and increasing your fresh goods spoilage, but failing to prepare sufficiently leads to significant lost sales. What's more, lost sales do not only apply to products that go out of stock, especially during extreme weather conditions when customers are more likely to make their decision on which store to visit based on the availability of a key product, for example bottled water, snow shovels, quality barbecue meats or candles. So how can retailers optimally prepare for weather-related fluctuations?
Here's a story familiar to anyone who does research in data science or machine learning: (1) you have a brand-new idea for a method to analyze data (2) you want to test it, so you start by generating a random dataset or finding a dataset online.(3) You apply your method to the data, but the results are unimpressive. And you introduce a hyperparameter into your method so that you can fine-tune it, until (5) the method eventually starts producing gorgeous results. However, in taking these steps, you have developed a fragile method, one that is sensitive to the choice of dataset and customized hyperparameters. Rather than developing a more generaland robust method, you have made the problem easier.
The unpublished work was presented at the Society for Neuroscience's annual meeting in Washington, D.C. It's one example of different kinds of learning that researchers would like to develop in AI -- and one based on aspects of human intelligence that computers haven't mastered yet. The approach is among a few being tried but one that some researchers are excited about because, as Hassabis recently wrote, "[The human brain is] the only existing proof that such an intelligence is even possible." "A lot of the machine learning people now are turning back to neuroscience and asking what have we learned about the brain over the last few decades, and how we can translate principles of neuroscience in the brain to make better algorithms," says Saket Navlakha, a computer scientist at the Salk Institute for Biological Sciences. Last week, he and his colleagues published a paper suggesting that incorporating a strategy used by fruit flies to decide whether to avoid an odor it hasn't encountered before can improve a computer's searches for similar images. The big question for all AI approaches: What problem is a particular algorithm best suited to solve, and will it be better than other AI techniques?
During the 2008 financial crisis, the banking industry realized that their machine learning algorithms were based on flawed assumptions. So financial system regulators decided that additional controls were needed, and regulatory requirements for "model risk" management on banks and insurers were introduced. Banks also had to prove that they understood the models they were using, so, regrettably but understandably, they deliberately limited the complexity of their technology, resorting to generalized linear models that offered simplicity and interpretability above all else. In the past several years, machine learning and AI have made enormous strides in accuracy. Yet regulated industries (like banking) remain hesitant, often prioritizing regulatory compliance and algorithm interpretability over accuracy and efficiency.
The high barrier to entry prevents many companies from tapping into the full potential of machine learning. But what if you could make it more accessible? We're in the midst of a data explosion, with today's enterprises amassing goldmines of information (25 quintillion bytes of data every day, according to some reports). But what exactly are they doing with this data? Considering the volume of data being collected is quickly becoming unmanageable, now is a good time to shift from manual machine learning to a cognitive approach.
Use Machine Learning to boost IoT efficacy says Forrester. A new report from Forrester, "Put Data to Work in the Industrial Internet of Things" advises CIOs to leverage machine learning to turn the tsunami of data obtained in Internet of Things (IoT) deployments into actionable insights. Successful companies in the industrial sector that are doing this are not only predicting problems and opportunities before they occur but are also developing new revenue streams during their digital transformation. Large volumes of data are required to train and then exploit machine learning algorithms, and fortunately, that data is now easily accessible, especially as IoT gains traction in industries. Machine learning is becoming a powerful tool in efforts to win, serve, and retain customers.
– Any one working within industries like the mobility, fintech, mobile money, payments, banking or InsureTech with little knowledge of data science is actually sitting on gold mine to explore and show what Data Science / AI can do for that company. Today every company on this planet collect vast quantities of data on a daily basis or even per second. For example credit card issuers with every credit card swipe and completed transaction capture critical customer information, In case of mobile payments/money the same thing happen or even in banks same scenarios. However, the raw data alone does not generate the insights needed to drive business decisions or simply not good enough at all. It's the proper analysis of this data that unlocks its true value.
With Artificial Intelligence and machine learning picking up worldwide popularity over most recent couple of years, more and more organizations are racing to implement the concept, and technology enthusiast taking a hang of the idea. From the machine learning algorithms of Netflix to the utilization of Artificial Intelligence in self driving cars, there has been a wide reception of these technology over every one of the sectors. The expanding popularity drove us to compile the machine learning algorithms that are most generally utilized and ought to be known by every data scientist, developers, engineers and each one of those anxious to investigate more about the field. In this way, here goes the infographic visualization of the top 10 machine learning algorithms that every data scientist must know.
Stanford researchers have developed an algorithm that offers diagnoses based off chest X-ray images. A paper about the algorithm, called CheXNet, was published Nov. 14 on the open-access, scientific preprint website arXiv. "Interpreting X-ray images to diagnose pathologies like pneumonia is very challenging, and we know that there's a lot of variability in the diagnoses radiologists arrive at," said Pranav Rajpurkar, a graduate student in the Machine Learning Group at Stanford and co-lead author of the paper. "We became interested in developing machine learning algorithms that could learn from hundreds of thousands of chest X-ray diagnoses and make accurate diagnoses." The work uses a public data set initially released by the National Institutes of Health Clinical Center on Sept. 26.