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) …
Cloudera, Inc. (NYSE: CLDR), the modern platform for machine learning and analytics, optimized for the cloud, announced that Komatsu, a leading global heavy equipment manufacturer, has implemented a cloud-based Industrial Internet of Things (IIoT) analytics platform powered by Cloudera Enterprise and Microsoft Azure. The platform enables Komatsu teams to help mining customers around the world continuously monitor the performance of some of the largest equipment used in surface and underground mining, increase asset utilization and productivity, and deliver essential resources including energy and industrial minerals for the global economy. Komatsu's JoySmart Solutions is an IIoT-based service that helps customers optimize machine performance using machine data and analytics. The JoySmart platform ingests, stores and processes a wide variety of data collected from mining equipment operating around the globe, often at very remote locations in harsh conditions. Types of equipment monitored includes longwall mining systems, electric rope shovels, continuous miners and wheel loaders.
A number of weeks ago I solicited feedback from my LinkedIn connections regarding what their typical day in the life of a data scientist consisted of. The response was genuinely overwhelming! Sure, no data scientist role is the same, and that's the reason for the inquiry. So many potential data scientists are interested in knowing what it is that those on the other side keep themselves busy with all day, and so I thought that having a few connections provide their insight might be a useful endeavour. What follows is some of the great feedback I received via email and LinkedIn messages from those who were interested in providing a few paragraphs on their daily professional tasks.
Imagine a digital banking experience where we can identify ourselves with absolute certainty, simply by being ourselves. Or an online journey where the authentication process is tailored precisely to the risk posed by the transaction itself. For consumers trapped in a seemingly endless cycle of usernames, passwords and additional security questions – not to mention blocks imposed on perfectly legitimate payments and transfers – it's clearly an attractive proposition. Banks too should find the vision compelling; digitalisation has transformed their marketplace. But, to date, a successful marriage between seamless customer experiences and robust cyber security has proven elusive as traditional risk assessment and authentication solutions have failed to keep pace with the sheer volume of online banking transactions and the scale and sophistication of hacking attacks.
California based company Cognoa is using machine learning to detect cognitive disorders in children up to 13 months earlier than traditional diagnosis methods. The company's VP of data science, Halim Abbas, told Which-50 a machine learning approach is ideal for detecting developmental delays. "Machine learning algorithms can ingest very large numbers of historical patient records, and use them to capture incredibly subtle patterns that might indicate the presence of cognitive disorders." "Highly resilient to noise and subjectivity" the process ultimately produces models that can approximate the understanding of what constitutes autism from the many different doctors who contributed to the dataset, Abbas said. "This allows Machine Learning screeners to succeed when applied at complex assessments like autism spectrum disorder, which can present a wide and highly variant set of behavioural phenotypes."
To beat Big Data, according to German electronics company Robert Bosch, we need to tier the solution by making every level smart -- from edge sensors to concentration hubs to analytics in the cloud. Luckily, we have the smart sensors of the brain -- eye, ears, nose, taste-buds and touch sensitivity -- as the smartest model in the universe (as we know it) after which to fashion our electronic Big Data solutions to the Internet of Things (IoT), said Marcellino Gemelli, head of business development at Bosch Sensortec. "We need to feed our Big Data problems into a model generator based on the human brain, then use this model to generate a prediction of what the optimal solution will look like," Gemelli told the attendees at the recent SEMI MEMS & Sensor Executive Congress (MSEC). "These machine learning solutions will work on multiple levels, because of the versatility of the neuron." Neurons are the microprocessors of the brain -- accepting thousands of Big Data inputs, but outputting a single voltage spike down their axon after receiving the right kind of input from thousands of dendrites mediated by memory synapses.
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As someone on Twitter said, if "bots" was on your Build 2016 drinking game card, you'd be long dead. But while Microsoft is all about getting developers to create intelligent app companions to make our lives easier, is there any impressive machine learning the Redmond firm is ready to show off right now? The answer, surprise, is yes. Besides ordering Domino's pizzas, Microsoft has been tinkering with its Azure-based tools to recognize age, gender, emotion and individuals by name. Remember the great/awful How Old Do I Look? website introduced at last year's Build?
I'm a DeepLearning enthusiast, an independent researcher in the filed of Artificial Intelligence. I work as financial technologist – FinTech expert. Have worked in the mobile payments for merchants & billers domain, traditional payments, cross border remittances (on mobile), e-commerce payments and mobile financial services (MFS). Now amalgamating artificial intelligence with FinTech at aggressive pace to pull some good values of data science, machine learning, deep learning and artificial neural networks techniques. Idea is to bring AI benefits to the real world to help consumers & businesses as it can solve every single major difficulty in today's world.
To help increase developers' productivity and simplify app development, Microsoft has announced new data platform technologies and cross-platform developer tools. The company launched a new AI-powered platform "Azure Databricks" during an event for developers late Thursday. Designed in collaboration with the founders of Apache Spark, Azure Databricks analytics platform delivers one-click setup, streamlined workflows and an interactive workspace. The platform will enable organisations to provide self-service analytics and machine learning over all data with enterprise-grade performance and governance, Microsoft said in a statement. "With today's intelligent cloud, emerging technologies like Artificial Intelligence (AI) have the potential to change every facet of how we interact with the world," said Scott Guthrie, Executive Vice President.