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) …
Artificial intelligence and machine learning, which found solid footing among the hyperscalers and is now expanding into the HPC community, are at the top of the list of new technologies that enterprises want to embrace for all kinds of reasons. But it all boils down to the same problem: Sorting through the increasing amounts of data coming into their environments and finding patterns that will help them to run their businesses more efficiently, to make better businesses decisions, and ultimately to make more money. Enterprises are increasingly experimenting with the various frameworks and tools that are on the market and available as open source software, in both small scale experiments run by a growing number of data scientists who have the expertise to find the valuable information the growing lakes of data and in full blown production deployments that are, conceptually, every bit as sophisticated as what the hyperscalers are deploying. The top cloud service providers and hyperscalers have for several years embrace data-driven AI and machine learning techniques and built their own internal frameworks and platforms that enable them to quickly take advantage of them. But as the technologies begin to cascade into more mainstream enterprises, the complexity of software and systems are throwing roadblocks in front of initiatives aimed at leveraging AI and machine learning for the good of the business.
In every federal agency, critical insights are hidden within the massive data sets collected over the years. But because of a shortage of data scientists in the federal government, extracting value from this data is time consuming, if it happens at all. Yet with advances in data science, artificial intelligence (AI) and machine learning, agencies now have access to advanced tools that will transform information analysis and agency operations. From predicting terror threats to detecting tax fraud, a new class of enterprise-grade tools, called automated machine learning, have the power to transform the speed and accuracy of federal decision-making through predictive modeling. Technologies like these that enable AI are changing the way the federal government understands and makes decisions.
Using and exploiting artificial intelligence (AI), is a goal for many enterprises around the world. Of course, before you can begin working with the cognitive technology, a number steps have to be taken. For starters, AI requires machine learning and machine learning, requires analytics. And to work with analytics effectively, you need a simple, elegant data, or, information architecture (IA). In other words, there is no AI without IA.
At the end of 2017, there will be 8.4 billion connected things in use worldwide up 31 percent from 2016, and this figure is expected to reach 20.4 billion by 2020. When Internet of Things (IoT) as an industry took off in India, it spawned a host of startups selling edge devices that could gather and crunch data from corporate customers. These startups ran into one fundamental problem, which was data lifting. The data was so voluminous that these startups took so much time to organise them that they ran out of money to keep the companies afloat. In the end, their services were just organising data for customers with very little insights.
It was a memorable year, to be sure, with plenty of drama and unexpected happenings in terms of the technology, the players, and the application of big data and data science. As we gear up for 2018, we think it's worth taking some time to ponder about what happened in 2017 and put things in some kind of order. Here are 10 of the biggest takeaways for the big data year that was 2017. Teradata, for instance, found that 80% of enterprises are already investing in AI, which backed similar findings from IDC. Nevertheless, the same old challenges that kept big data off Easy Street also emerged to cool some of the heat emanating from AI.
Deep learning has given us tremendous new powers to spot patterns hidden in great globs of data. For some challenges, neural networks can even outperform top human experts. However, despite all the progress the new approach represents and the hope that it will lead us to actual artificial intelligence, there are big limits on the practical application of deep learning. Deep learning has emerged as the latest "easy button" for big data analytics. The thinking seems to go like this: Got a lot of data to analyze?
A non-comprehensive list of awesome things other people did in 2014 Last year I made a list off the top of my head of awesome things other people did. I loved doing it so much that I'm doing it again for 2014... The Current State of Machine Intelligence I spent the last three months learning about every artificial intelligence, machine learning, or data related startup I could find -- my current list has 2,529 of them to be exact. Yes, I should find better things to do with my evenings and weekends but until then... The Things I Wish I Knew - Lessons Learned from Making Data Product Talk from DJ Patil, Greylock Partners as part of this seminar series featuring dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field A Data Analyst's Blog Is Transforming How New Yorkers See Their City It may have been the fire hydrants that certified Ben Wellington as the king of New York's "open data" movement.
At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. To close out 2017, we recently asked some of the leading experts in Big Data, Data Science, Artificial Intelligence, and Machine Learning for their opinion on the most important developments of 2017 and key trends they 2018. This post, the first in this series of such year-end wrap-ups, considers what happened in Machine Learning & Artificial Intelligence this year, and what may be on the horizon for 2018. "What were the main machine learning & artificial intelligence related developments in 2017, and what key trends do you see in 2018?"
During my Machine Learning studies I developed a taste for fast Machine Learning pipelines. Since python provides coding versatility it is an obvious choice for this endeavor. Scikit-Learn is an excellent framework to use any type of algorithm you might want to, i.e. most Machine Learning algorithms provide an interface for it. One popular example for this is xgboost. Although the interface exists it lacks a lot of functionality, e.g.
Streaming services have changed the way in which we experience content. While recommendation systems previously focused on presenting you with content you might want to purchase for later consumption, modern streaming platforms have to focus instead on recommending content you can, and will want to, enjoy in the moment. Since any piece of content is immediately accessible, the streaming model enables new methods of discovery in the form of personalized radios or recommendation playlists, in which the focus is now more on generating sequences of similar songs that go well together. With now over 700 million songs streamed every month, Anghami is the leading music streaming platform in the MENA region. What this also means, is that the amount of data generated by all those streams proves to be an invaluable training set that we can use to teach machine learning models to better understand user tastes, and improve our music recommendations.