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) …
Machine learning algorithms now underlie much of the software we use, helping to personalize our news feeds and finish our thoughts before we're done typing. But as artificial intelligence becomes further embedded in daily life, expectations have risen. Before autonomous systems fully gain our confidence, we need to know they are reliable in most situations and can withstand outside interference; in engineering terms, that they are robust. We also need to understand the reasoning behind their decisions; that they are interpretable. Aleksander Madry, an associate professor of computer science at MIT and a lead faculty member of the Computer Science and Artificial Intelligence Lab (CSAIL)'s Trustworthy AI initiative, compares AI to a sharp knife, a useful but potentially-hazardous tool that society must learn to weild properly.
Today, Machine Learning is one of the most important trends in every area of software engineering. No longer limited to researchers and analysts, it's a vital part of everything from cybersecurity to web development. To help you get started with Machine Learning, we've put together this list of 5 free Machine Learning eBooks from Packt. You can download as many of them as you like -- all you'll need to do is register when you download your first title. But there's an important reason it's the first free eBook on this list: Python is the go-to language if you want to develop Machine Learning models.
There has been a lot of technological advancements paving their ways into the mainstream app development industry. One of those witnessed trends has been artificial intelligence. It's not something new, there are already a plethora of Android apps and app developers simultaneously. Not all the companies have ingrained the advancements in their development process. They still follow the traditional antiquated methods of the development.
Years ago, when I was taking my first steps in computer programming, coding was for geeks and computer programs had limited use. Development tools were very crude, writing code was hard (remember Assembly, C, Pascal?), compiling and linking was a nightmare (MAKE files anyone?), and debugging was even worse. Long story short, programming was not for the faint of heart. You needed nerves of steel and had to patiently fail over and over before you got the hang of writing good code. But as software gradually rose in prominence, the entry barrier to programming lowered.
For example, consider that most (if not all) test automation tools run tests for you and deliver results. Most don't know which tests to run, so they run all of them or some predetermined set. So what if an AI-enabled bot can review the current state of test status, recent code changes, code coverage, and other metrics, decide which tests to run, and then run them? Bringing in decision-making that's based on changing data is an example of applying AI (And Parasoft does this, by the way). The software is effectively able to replace the developer/tester in the decision-making process.
As 2018 comes to a close, it's interesting to watch various parties attempting to analyze the reasons behind Microsoft's "comeback." It's the change in culture, some argue. It's the focus on open source, say others. It's because Microsoft is (or isn't) the next IBM, armchair pundits pundicize. My take: Microsoft has evolved into an interesting blend of practicality and aspiration.
When you think of "data science" and "machine learning", do the two terms blur together, like Currier and Ives or Sturm and Drang? If so, you've come to the right place. This article will clarify some important and often-overlooked distinctions between the two to help you better focus your learning and hiring. Machine learning has seen much hype from journalists who are not always careful with their terminology. In popular discourse, it has taken on a wide swath of meanings and implications well beyond its scope to practitioners.
The Commonwealth Scientific and Industrial Research Organisation's (CSIRO) Data61, alongside IAG and the University of Sydney, has created a new artificial intelligence (AI)-focused institute, aimed at exploring the ethics of the emerging technology. The Gradient Institute, Data61 explained, is an independent non-profit charged with researching the ethics of AI, as well as developing ethical AI-based systems, focused essentially on creating a "world where all systems behave ethically". "By embedding ethics into AI, we believe we will be able to choose ways to avoid the mistakes of the past by creating better outcomes through ethically-aware machine learning," Institute CEO Bill Simpson-Young said. "For example, in recruitment when automated systems use historical data to guide decision making they can bias against subgroups who have historically been underrepresented in certain occupations. "By embedding ethics in the creation of AI we can mitigate these biases which are evident today in industries like retail, telecommunications, and financial services." In addition to research, it is expected the new institute will also explore the ethics of AI through practice, policy advocacy, public awareness, and training, specifically where the ethical development and use of AI is concerned. The institute will use research findings to create open source ethical AI tools that can be adopted and adapted by business and government, Data61 said in a statement Thursday. "As AI becomes more widely adopted, it's critical to ensure technologies are developed with ethical considerations in mind," Data61 CEO Adrian Turner added. "We need to get this right as a country, to reap the benefits of AI from productivity gains to new-to-the-world value." See also: AI'more dangerous than nukes': Elon Musk still firm on regulatory oversight Speaking with ZDNet during Data61's annual conference this year in Brisbane, acting director of Engineering and Design at Data61 Hilary Cinis said ethics is all about the reduction of harm. One way around ingrained ethical bias, she said, was to ensure that teams building the algorithms are diverse. She said a "cultural rethink" around development needs to happen. Similarly, Salesforce user research architect Kathy Baxter said at the Human Rights & Technology Conference in Sydney earlier this year that one main problem that arises is bias can be difficult to see in data. Equally complex, she said, is the question of what it means to be fair. "If you follow the headlines, you'll see that AI is sexist, racist, and full of systematic biases," she said. "AI is based on probability and statistics," she continued. "If an AI is using any of these factors -- race, religion, gender, age, sexual orientation -- it is going to disenfranchise a segment of the population unfairly and even if you are not explicitly using these factors in the algorithm, there are proxies for them that you may not even be aware of.
Since 2016, IBM has offered online access to a quantum computer. Anyone can log in and execute commands on a 5-qubit or 14-qubit machine located in Yorktown Heights, New York, from the comfort of their own home. This month, I finally tried it--nervously. I did not know what I was doing and worried I might break the hardware. "You won't mess anything up," IBM physicist James Wootton assured me via Skype.