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) …
This year, we've had the chance to chat in depth high up the data scientist totem pole on how to operationalize AI projects. We've found that data science is the fundamental building block. We've also found that, while data and insight are the fuel, collaboration and agility are lubricants that grease the skids. Ultimately, that places a premium on the people and process sides of AI projects. The spotlight might be on the skills, the access to powerful GPUs, and the frameworks for developing the algorithms.
I'm reposting this blog (with updated graphics) because I still get many questions about the difference between Business Intelligence and Data Science. I recently had a client ask me to explain to his management team the difference between a Business Intelligence (BI) Analyst and a Data Scientist. I frequently hear this question, and typically resort to showing Figure 1 (BI Analyst vs. Data Scientist Characteristics chart, which shows the different attitudinal approaches for each)... But these slides lack the context required to satisfactorily answer the question – I'm never sure the audience really understands the inherent differences between what a BI analyst does and what a data scientist does. The key is to understand the differences between the BI analyst's and data scientist's goals, tools, techniques and approaches.
Programmed by Arthur Samuel, this big data discipline of artificial intelligence replaces the tedious task of trying to understand the problem well enough to be able to write a program, which can take much longer or be virtually impossible. Techopedia defines the discipline of machine learning as "an artificial intelligence (AI) discipline geared toward the technological development of human knowledge. Machine learning allows computers to handle new situations via analysis, self-training, observation and experience. Machine learning facilitates the continuous advancement of computing through exposure to new scenarios, testing and adaptation, while employing pattern and trend detection for improved decisions in subsequent (though not identical) situations." In 1959, IBM employee Arthur Samuel wanted to teach a computer to play checkers.
Having worked in and with the automotive industry for around 25 years, the challenges that OEMs face given their size and structures often inhibit the business agility needed to provide lasting customer value in an age of digital disruption. The focus has always been more skewed toward the product experience and product features and defining greatness by "number of cars." Also: From big data to AI: Where are we now? Mobility as a driver for change has existed for more than 10 years, but the increased competitiveness from nontraditional players has created new challenges for OEMs and forced them to rethink their role. It has produced more service-oriented ideas such as car-sharing schemes, partnerships with ride-hailing services, and closer collaboration with urban planners.
Makes me feel sad for the rest. Actually, that's a movie ("The Spy that Loved Me") that Netflix recommends for me since I'm a James Bond junkie and Netflix knows that. In fact, Netflix knows a lot about me as it knows a lot about all of its viewers, which is one reason why Netflix is a Wall Street darling and has rewarded its stockholders very well over the past couple of years (see Figure 1). But Netflix isn't doing anything that other organizations cannot do. To replicate Netflix's business success starts with thinking differently about the role of data and analytics in powering the organization's business.
Panoply, the smart cloud data warehouse built for business intelligence, is excited to announce its status as a finalist in Microsoft and Calcalist's Artificial Intelligence and Big Data Startup Competition. In a stiff competition, Microsoft will host representatives from Panoply and the other finalists at its Seattle headquarters. The first and second place winners will be invited by Calcalist to participate in the newspaper's Berlin conference in 2019, where the companies will meet with potential partners and investors. Panoply's CEO and co-Founder Yaniv Leven said, "We're proud to be announced as a finalist in the Artificial Intelligence and Big Data Startup competition. So far, we've been amazed at the quality of the companies participating and we're psyched to be pitching at the finals. We're a team of engineers and fighters, we set lofty goals and conquer massive challenges to prove how bad our innovative desire is."
AMD and Singapore's Nanyang Technological University (NTU) have jointly set up a S$4.8 million (US$3.5 million) lab aimed at building up local skillsets in data science and artificial intelligence (AI). The new facility would support the university's undergraduate programme encompassing the two technology areas and enable its students to experience real-world applications, such as the development of software models used in security including identification and motion detection. They also would be able to learn about big data analytics applications often used in large enterprise environments and develop, for instance, clinical support software that used analytics tools to assist medical diagnosis. Country's government has introduced initiatives to train 12,000 people in artificial intelligence skillsets, including industry professionals and secondary school students. Under the partnership, NTU students that worked on research projects and machine learning applications at the lab would be tapping AMD's Radeon Open Compute platform, said the two organisations in a statement Friday.
Experts predict that the universe of data--or'dataverse'--will reach 180 zettabytes by 2025. Bernard Marr, author of Data Strategy: How to Profit from a World of Big Data, Analytics and The Internet of Things, offers some perspective, noting that 90 per cent of existing data in the world has been generated in the last two years. Unfortunately, the volume and variety of data available does not always equate to value. Harvard University's Gary King suggests that "Big data is not about the data!" Instead, he writes, "Although the increase in the quantity and diversity of data is breath-taking, data alone does not a Big Data revolution make. The progress in analytics making data actionable over the last few decades is also essential."
Systems or machines that mimic human intelligence. Often used interchangeably with its subfields, including machine learning and deep learning, artificial intelligence has become a catch-all term for applications that perform complex tasks that once required human input, such as chatting online with customers or playing chess.