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) …
SenseTime has announced the completion of a $620 million Series C funding round led by Fidelity International, Hopu Capital, Silver Lake, and Tiger Global, which the company says values it at more than $4.5 billion, making it the world's largest AI unicorn. The announcement comes just over a month after a $600 million investment from Alibaba, SenseTime's largest stakeholder, as part of the same round. Other participants in the " " portion of the round include Qualcomm Ventures. SenseTime has now raised $1.6 billion, and the company achieved profitability in 2017 with its third consecutive year of 400 percent year-over-year growth. The company also says its has increased its business contract revenue more than tenfold in 2018 up to the beginning of May.
Silicon Valley has a term for startups that reach the $1 billion valuation mark: unicorns. It suggests not only that hugely successful startups are rare, but also that there's something unreal about them. Founded by a 19-year-old Stanford dropout, Elizabeth Holmes, who went on to become the world's youngest self-made female billionaire, it raised nearly a billion dollars from investors and was valued at $10 billion at its peak. It claimed to have developed technology that dramatically increased the affordability, convenience, and speed of blood testing. It partnered with Safeway and Walgreens, which together spent hundreds of millions of dollars building in-store clinics that were to offer Theranos tests. Tens of thousands of Americans had their blood tested by its proprietary technology. The problem was that Theranos' technology was never close to ready.
Darktrace has become a rarity in the British tech scene: a Unicorn. The cybersecurity company, which provides what it calls a network "immune system" powered by artificial intelligence, hit a $1.25 billion valuation last month, two sources close to the deal told Forbes. The new valuation came after a secondary round of financing in which former investors sold off their stakes. Vitruvian Partners, sources said, is the acquirer of the stock. How did Darktrace find itself at the vaunted Unicorn status?
Regardless of how artificial intelligence (AI) is defined, there is little doubt that this resource can be of great value, especially in big data applications. Undoubtedly, AI is fast becoming a major technological tool for prescriptive analytics, the step beyond predictive analytics that helps us determine how to implement and/or optimize optimal decisions. In business applications, it can assess future risks, quantify probabilities and in so doing, give us insights how to improve market penetration, customer satisfaction, security analysis, trade execution, fraud detection and prevention, while proving indispensable in land and air traffic control, national security and defence. There are also a host of healthcare applications such as patient-specific treatments for diseases and illnesses. Recently, the popular concept of "Singularity" was perceived by computer scientists.
A recent study from Bain indicates that automation will fuel an economic boom for the next 10 -15 years, far disruptive than anything we have seen in the last 60 years. Enterprises are seeking to invest in automation and this newest wave will stimulate further investment estimated to be $8T. Poised to capitalize on this unprecedented interest and market opportunity is UiPath. Its focus is on robotic process automation through its software platform that is designed for enterprise companies to automate repetitive tasks. Streamlining workflows in a scalable manner and saving corporations money, the platform allows you to train software robots, acting as an operating system for your automation needs.
Data scientists have been called "unicorns" because finding the right person with the right set of skills -- including coding, statistics, machine learning, database management, visualization techniques, and industry-specific knowledge -- could be practically impossible. But machine learning and big data itself may be making those unicorns as obsolete as they are mythical. New machine learning algorithms can autonomously analyze data and identify patterns, even interpret the data and produce reports and data visualizations. While most people can see how certain information would be useful and what sort of insights might be derived from it, most lack the technical skills to perform the analytics. They might not have the computers that are able to carry out the large volume of calculations quickly enough to take action, but more often they lack the analytical skills to tell that computer what to do.
The global tech startup scene is a noisy, crowded space. And then there are the stories of those entrepreneurs whose ideas have endured into something truly transformational; Amazon, Apple, Google, you name it--many of the biggest and most influential companies in the world today were born of this heritage. "One thing we can all agree on: The key attribute of a startup is its ability to grow," wrote Forbes' Natalie Robehmed. And as my former CEO, Mark Jones, used to say, "All big companies were once small companies too. The only difference is that they grew up."
Making a machine learning model usually takes a lot of crying, pain, feature engineering, suffering, training, debugging, validation, desperation, testing and a little bit of agony due to the infinite pain. After all that, we deploy the model and use it to make predictions for future data. We can run our little devil on a batch of data once in an hour, day, week, month or on the fly depending on the situation and use case. Let's take a look at an example related to an online sport betting recommender engine. The goal of that engine is to predict whether the user will play a particular selection on a game or not (e.g.
Making a machine learning model usually takes a lot of crying, pain, feature engineering, suffering, training, debugging, validation, desperation, testing and a little bit of agony due to the infinite pain. After all that, we deploy the model and use it to make predictions for future data. We can run our little devil on a batch of data once in an hour, day, week, month or on the fly depending on the situation and use case.