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) …
Researchers from Alphabet-owned company DeepMind say a new AI can ingest a patient's medical history and predict, with 90 percent accuracy, whether they're going to need dialysis for acute kidney injury 48 hours before it occurs. "Currently we pick these things up too late and harm is caused to patients, and we think there's a real opportunity for these AI systems to be able to predict and prevent rather than just what currently happens, which is clinicians almost firefighting and running around problems that have already developed," DeepMind clinical lead Dominic King told Wired. The team fed health data from more than 700,000 Veterans Affairs hospital patients across the U.S. to their neural network. Their results were promising, according to a paper about the research published Wednesday in the journal Nature: the system can even tell doctors what piece of medical data tipped it off that a kidney crisis was imminent. But while the system is speedy, it's way too trigger-happy: it reported two false positives for every correctly identified kidney injury.
It's not who has the best algorithm that wins; It's who has the most data -- Andrew Ng. Image classification is the task of assigning an input image one label from a fixed set of categories. This is one of the core problems in Computer Vision that, despite its simplicity, has a large variety of practical applications. In this blog I will be demonstrating how deep learning can be applied even if we don't have enough data. I have created my own custom car vs bus classifier with 100 images of each category.
Next-generation vehicles such as drones have a hard time landing. Drone controllers usually bring the drone near the ground and then drop it. How low the drone can be brought down depends on the aerodynamics of the drone and other reactions from the ground. Since drones of the future will be carrying medicines and other fragile instruments into mysterious landscapes or hilly areas, dropping the drone isn't always desirable. To address this problem of smooth landing, researchers at CalTech's Center for Autonomous Systems and Technologies (CAST), have imbibed neural networks into their approaches.
If you do know what a Data Scientist is, you are rare to find, as since even the most experienced professionals still have difficulty defining the scope of the area. One possible delimitation is that the data scientist is the person responsible for producing predictive and / or explanatory models using machine learning and statistics.
The Apache Software Foundation (ASF) recently announced that SINGA, a framework for distributed deep-learning, has graduated to top-level project (TLP) status, signifying the project's maturity and stability. SINGA has already been adopted by companies in several sectors, including banking and healthcare. Originally developed at the National University of Singapore, SINGA joined ASF's incubator in March 2015. SINGA provides a framework for distributing the work of training deep-learning models across a cluster of machines, in order to reduce the time needed to train the model. In addition to its use as a platform for academic research, SINGA has been used in commercial applications by Citigroup and CBRE, as well as in several health-care applications, including an app to aid patients with pre-diabetes.
Artificial Intelligence (AI) and machine learning enter the research mainstream of biopharmaceutical companies, such as GlaxoSmithKline (GSK). GlaxoSmithKline (GSK) is creating a data-focused culture and a global machine-learning team. GlaxoSmithKline's (GSK's) data-first approach to drug discovery and development comes directly from chief executive officer (CEO) Emma Walmsley and chief scientific officer (CSO) Hal Barron. Their goal is doubling the chance of successful medicines being produced by using genetically validated targets. And that demands a strong team in artificial intelligence and machine learning (AI/ML).
A recent "deep learning" algorithm--despite having no innate knowledge of solar physics--could provide more accurate predictions of how the sun affects our planet than current models based on scientific understanding. For decades, people have tried to predict the impact of the sun on our planet's atmosphere. Up until now, algorithms based on solar physics have been used to predict the shifting density of Earth's atmosphere. But with so many variables affecting the complex and dynamic layers of gases around Earth, artificial intelligence (AI) could provide real improvements in this area because of its ability to handle vastly more complex data, with important implications for how we fly missions in Earth orbit. The conditions in space vary depending on the mood swings of the sun, known as "space weather."
Canada has received more than its usual share of attention for its AI capabilities. The country was either prescient or lucky in continuing to fund neural networks research when the US retreated from it in the 1970s and 80s. As a result, Canadian researchers like Geoffrey Hinton, Yann LeCun, and Yoshua Bengio pushed forward the methods we now call "deep learning." These three researchers won the 2018 Turing Award--often called the Nobel equivalent for computer science. Canada is also known in AI for its collegial, public/private ecosystems.
Summary: AML has been around since at least 2016 but only in the last year have Gartner and Forrester begun to offer their opinions. This has been a big year for AML (automated machine learning). A number of new players have emerged and pretty much everyone acknowledges that some level of automation is appropriate to enhance the productivity of your data science team. And no, data scientists shouldn't be alarmed by this trend. None of this approaches letting an untrained person push the button and have a useful model pop out.