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) …
We're excited to announce that the Machine Learning Blueprint is joining the IBM DataScience Community! We've always strived to source high quality content across the web and put deep thought into our curations. However, continually delivering this every week is not easy, so after two years of publishing, we decided to take a pause. Through our work with the IBM Community we identified an opportunity to join forces to grow a community of machine learning practitioners, we were thrilled at the prospect. Their mission was clear: provide a place for data scientists to interact with other experts, share support and insights and start dialogue around relevant topics.
Choosing the right investment strategy is a very important step towards reaching your financial goal, whether it is accumulating enough to buy a cottage house or living the life of a millionaire after retirement. The way you build and manage your portfolio largely depends on your appetite for risks and the available funds, as well as your personal preferences and understanding of the market. Thus, it is no surprise that there are quite a few investment strategies out there, and while quite many of them have been showing good results, there is still room for improvement. The booming artificial intelligence technology has been a boon for the financial industry, and in this article, we will show why some of the most popular investment strategies and AI are a match made in heaven. The artificial intelligence has turned into a buzzword these days, to the point where it can at times be difficult to figure out what exactly this umbrella term means.
Machine-learning chatbot systems can be exploited to control what they say, according to boffins from Michigan State University and TAL AI Lab. "There exists a dark side of these models – due to the vulnerability of neural networks, a neural dialogue model can be manipulated by users to say what they want, which brings in concerns about the security of practical chatbot services," the researchers wrote in a paper (PDF) published on arXiv. They crafted a "Reverse Dialogue Generator" (RDG) to spit out a range of inputs that match up to a particular output. Text-based models normally work the other way, where outputs are generated after having been given an input. For example, given the sentence "Hi, how are you?", a computer learns to output a response like "Fine, thank you" as it learns that is one of the most common replies to that question in training data.
Created in 2017 from the combination of two long-standing companies, Finastra has already emerged as one of the largest financial technology (fintech) companies in the world, building solutions to support banking and other financial services. Finastra has made innovation its hallmark, constantly seeking new ways to use technology to improve financial services for banks and customers alike. By forming a close working relationship with Microsoft, Finastra is now ushering in a new generation of fintech that puts humans at the center of these technological solutions. With Microsoft Azure, Finastra has elevated its open innovation platform, FusionFabric.cloud, The platform is built with the power of Microsoft machine learning and advanced analytics tools.
If you enjoy intellectual challenges and designing experiences that impact millions of people, enterprise UX could be right for you. In 2005, YouTube was born, Google had just acquired Android, Yahoo! was a popular search engine, and there was no Netflix, Twitter, or Spotify. In that same year, I was asked to build up and manage my first visual design team. In those still formative years of the internet and hence modern user experience (UX), it was unusual – at least in Europe – to find a visual designer trained in human-computer interaction. So, I hired what I could find: talented graphic designers, most of whom had experience creating work for print and the web, but who had no idea about designing software.
Computers outperform humans in image and object recognition. Big corporations like Google and Microsoft have beat the human benchmark on image recognition [1, 2]. On average, human makes an error on image recognition tasks about 5% of the time. As of 2015, Microsoft's image recognition software reached an error rate of 4.94%, and at around the same time, Google announced that its software achieved a reduced error rate of 4.8% . This was possible by training deep convolutional neural networks on millions of training examples from ImageNet dataset which contains hundreds of object categories .
Each day, we read more news about artificial intelligence (AI), machine learning (ML) and their uses for not only work but, more importantly, education. About a year ago, I started to research these areas. While I understood the concepts of both and could offer a decent definition, I was not able to easily identify what it might look like in today's classrooms. My first interaction with machine learning came some years ago when I worked on my Spanish translation coursework. Our focus was on the level of accuracy that ML-translation provided for students and for businesses looking to use these services.