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 has become a part of our life – its objectively huge potential is now obvious to everyone, more and more is being said about innovative products that give an idea of an AI-operated world of the future, but less so, about the risks associated with the introduction of such technologies – primarily because they are not considered relevant yet. Nevertheless, while developing solutions for data protection with the introduction of AI, we can confidently say that the relevance of these risks will become a problem not in years, but months from now. Starting with the fact that along with promising technological trends - machine learning and AI - the technologies of cyberthreats grow and develop at the same pace, if not faster – recent cases of WannaCry and NonPetya only prove that. AI algorithms, with all their advantages, have a fundamental problem – data sensitivity. The general weakness with most of the algorithms created so far is that they are trained not to understand information, but to recognize the right answers.
"We cannot be conscious of what we are not conscious of." Unlike the director leads you to believe, the protagonist of Ex Machina, Andrew Garland's 2015 masterpiece, isn't Caleb, a young programmer tasked with evaluating machine consciousness. Rather, it's his target Ava, a breathtaking humanoid AI with a seemingly child-like naïveté and an enigmatic mind. Like most cerebral movies, Ex Machina leaves the conclusion up to the viewer: was Ava actually conscious? In doing so, it also cleverly avoids a thorny question that has challenged most AI-centric movies to date: what is consciousness, and can machines have it?
It hasn't been an easy couple of years for algorithms. Increasingly populated with content decried as'fake news', 'clickbait', today's highly personalized social media feeds are coming under increasing fire for being filter bubbles, culminating in Mark Zuckerberg's highly public apology to Facebook's users at the start of this month. These shifts in media mirror concurrent developments in spheres as diverse as customer service and support, financial trading, healthcare, and more. Today, though, tech is fighting back – thanks to AI. After partnering with Automated Insights in 2014 – a natural language generation start-up – the Associated Press became one of the earliest adopters of AI within the media space.
The lifts rising to Yitu Technology's headquarters have no buttons. The pass cards of the staff and visitors stepping into the elevators that service floors 23 and 25 of a newly built sky scraper in Shanghai's Hongqiao business district are read automatically – no swipe required – and each passenger is deposited at their specified floor. The only way to beat the system and alight at a different floor is to wait for someone who does have access and jump out alongside them. Or, if this were a sci-fi thriller, you'd set off the fire alarms and take the stairs while everyone else was evacuating. But even in that scenario you'd be caught: Yitu's cameras record everyone coming into the building and tracks them inside.
Deep learning has provided the world of data science with highly effective tools that can address problems in virtually any domain, and using nearly any kind of data. However, the non-intuitive features deduced and used by deep learning algorithms require a very careful experimental design, and a failure to meet that requirement can lead to miserably flawed results, regardless of the quality of the data or the structure of the deep learning network. I first noticed such flaws almost ten years ago, when I applied algorithms that used non-intuitive features for the purpose of automatic face recognition. I noticed that when using the most common face recognition benchmarks at that time (FERET, ORL, YaleB, JAFFE, and others), the algorithms could identify the correct face even when using just a small seemingly blank part of the background, normally a small sub-image from the top-left corner of the original image, that does not contain any part of the face, hair, clothes, or anything else that could allow the recognition of a person (1). I ran the experiments like they were intended, but instead of using the full face images I used a very small part of the background taken from the top-left corner of each image.
In this post we are going to develop a Neural Network for training and detecting Image Digits(0-9). In this post we are going to develop a java recommender application with implicit feedback for an Online Retail. In this post we are going to develop an autocomplete component using Tries Data Structure and Collaborating Filtering to choose best ... Read More In this post we are going to develop a movie recommender java application using Collaborative Filtering Algorithm. In this post we are going to develop a Spark based Java Application which detects spam emails. In this post we are going to develop an application for the purpose of detecting spam emails.The algorithm which will ... Read More K-Means is one of the most famous and widely used algorithm on Machine Learning field.
Companies say the new tools make them more efficient and give employees more opportunities to do new kinds of work. But the software also is starting to take on management tasks that humans have long handled, such as scheduling and shepherding strategic projects. Researchers say the shift could lead to narrower roles for some managers and displace others. When Shell wanted help evaluating digital business models in the car-maintenance sector, executives plugged the project into an algorithm that scanned for available Shell staffers with the right expertise--and assigned the job with a click. Shell uses machine-learning software designed by Boston-based Catalant Inc. to match workers and projects.
By Ananth Narayanan, Myntra-Jabong A lot has been said about the impact of artificial intelligence (AI) and its ability to transform our lives. In the spectrum between enthusiasts and doomsday predictors, I am an enthusiast who believes that AI will transform business and decision-making. While computers are great at rule-based programs, the human brain performs way better with pattern matching and intuition. With AI, though, machines are now getting better at pattern-matching and are fundamentally changing how we comprehend data but are still a far cry from how the human brain functions. Several innovations are possible today because of advances in AI -- not just in algorithms, but because data and computing power are both growing exponentially.
Organizations are transforming their sales functions with artificial intelligence to stay ahead of the game. If you have not yet embraced the trend, you are missing a crucial competitive edge. The emergence of vast amounts of data from multiple sources and platforms, generating new information every minute, has given companies more consumer information than they've ever had before. Technology is getting smarter as it continues learning and optimizing recommendations. A study published in MIT Sloan Management Review reveals that 76% of early adopters are targeting higher sales growth with machine learning.
Editor's note: Welcome to Throwback Thursdays! Every third Thursday of the month, we feature a classic post from the earlier days of our company, gently updated as appropriate. We still find them helpful, and we think you will, too! You can find the original post here. If you're fresh from a machine learning course, chances are most of the datasets you used were fairly easy.