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) …
With the rise of Machine Learning inside industries, the need for a tool that can help you iterate through the process quickly has become vital. Python, a rising star in Machine Learning technology, is often the first choice to bring you success. So, a guide to Machine Learning with Python is really necessary. In my experience, Python is one of the easiest programming languages to learn. There is a need to iterate the process quickly, and the data scientist does not need to have a deep knowledge of the language, as they can get the hang of it real quick.
Revenue from AI software soared more than 63 per cent to $846m (£664m) last year amid a surge in firms investing in the new technology. Robotic process automation (RPA), which provides AI tools for businesses, is now the fastest-growing segment of the enterprise software market, with revenue set to reach $1.3bn in 2019, according to research firm Gartner. "The RPA market has grown since our last forecast, driven by digital business demands as organisations look for'straight-through' processing," said Fabrizio Biscotti, research vice president at Gartner. "Competition is intense, with nine of the top 10 vendors changing market share position in 2018." The top five RPA vendors controlled 47 per cent of the market in 2018, though the vendors ranked sixth and seventh posted trip-digit revenue growth in a sign of heightening competition.
LOS ALAMOS, N.M., July 18, 2019--Three teams who applied novel machine learning methods to successfully predict the timing of earthquakes from historic seismic data are splitting $50,000 in prize money from an open, online Kaggle competition hosted by Los Alamos National Laboratory and its partners. "Crowdsourcing for new approaches in earthquake forecasting helps us leverage a wide range of expertise in addressing one of the most important problems in Earth science, because of the devastating consequences of large quakes," said Bertrand Rouet-Leduc, a Los Alamos researcher who prepared the data for the competition. "The winning teams' results could have the potential to improve earthquake hazard assessments that could save lives and billions of dollars in infrastructure." Current scientific studies related to earthquake forecasting focus on three key points: when the event will occur, where it will occur, and how large it will be. The Kaggle competition provided a challenging dataset that was based on previously published laboratory analysis, to give the competitors a taxing project to explore.
As the world descends into geopolitical competition, other powers increasingly challenge European countries' ability to defend their interests and values. Russia is willing to weaponise energy supplies, cyber capabilities, and disinformation; China invests strategically and uses state capitalism to skew the market; Turkey instrumentalises migration; Saudi Arabia leverages its energy resources. And the Trump administration is willing to exploit European dependence on the transatlantic security alliance and the dollar to achieve short-term policy goals. What unites these disparate powers is their unwillingness to separate the functioning of the global economy from political and security competition. The EU has the market power, defence spending, and diplomatic heft to end this vulnerability and restore sovereignty to its member states.
I came across all kinds of advice when I was looking for a data science internship. But surprisingly, not many people talk about how to land that internship. My learning journey during my internship with Analytics Vidhya was equal parts challenging and fulfilling. I realized how vast and complex data science is and how unprepared I was for a full-time role. My path to become a data scientist would have been far more arduous and difficult one if I hadn't first interned. Even for experience people – internships are a very effective way to break into data science. We have now seen so many successful transitions enabled by internships. If you are looking for tips to prepare yourself for a data science internship, then you've come to the right place! In this article, I've drawn on my experience on the key aspects you need to know to land your first internship in data science. Each section is filled with plenty of tips, tricks, and resources. It won't be easy – but you would know what needs to be done. If you are looking for a guided journey with mentorship – check out our Certified Program: Data Science for Beginners (with Interviews) .
Most data scientists and the organizations that employ them don't seem to understand how data science is actually done, nor what it is exactly. They sort of jumped on the bandwagon -- without really understanding it, nor why it was important to them in a very visceral way. Science is not merely predictive -- at its heart, it is explanatory as well as diagnostic. Science leads to engineering -- a systematic mathematical approach to creating technology solutions based on the exploitation of some natural phenomenon. Winning Kaggle competitions is not data science; though, it is a reasonable start, I suppose – even though the best models in Kaggle are actually built by machines running genetic algorithms, where natural selection drives the outcome.
In 2016, AI became part of China's national technology development program to boost AI research and development and enter formally the race to become a leading AI nation. That China has made tremendous progress highlights a report published by Tsinghua University. According to the report, "China leads the world in AI papers, has become the largest owner of AI patents, has the world's second largest AI talent pool, and the highest venture investment in AI." China is running a neck-and-neck race with the United States, followed by countries like Japan and South Korea. Since 2018, however, a debate has also been underway in China about ethical and regulatory questions concerning the use of AI.
At its core, AI is about automation and augmentation: it improves upon and speeds up many of the core processes of a business. Whether your business uses AI or not, it's going to be impacted by artificial intelligence in 2019. It's important to know how. As of early January, 61% of all businesses had already started implementing AI-based technologies. Even if you aren't using AI to streamline your operations and reduce your costs, your competition is.
Computer scientists have developed a card-playing bot, called Pluribus, capable of defeating some of the world's best players at six-person no-limit Texas hold'em poker, in what's considered an important breakthrough in artificial intelligence. Two years ago, a research team from Carnegie Mellon University developed a similar poker-playing system, called Libratus, which consistently defeated the world's best players at one-on-one Heads-Up, No-Limit Texas Hold'em poker. The creators of Libratus, Tuomas Sandholm and Noam Brown, have now upped the stakes, unveiling a new system capable of playing six-player no-limit Texas hold'em poker, a wildly popular version of the game. In a series of contests, Pluribus handedly defeated its professional human opponents, at a level the researchers described as "superhuman." When pitted against professional human opponents with real money involved, Pluribus managed to collect winnings at an astounding rate of $1,000 per hour.