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) …
In the past few years, deep learning is all the fuss in the tech industry. To keep up on things I like to get my hands dirty implementing interesting network architectures I come across in article readings. Few months ago I came across a very nice article called Siamese Recurrent Architectures for Learning Sentence Similarity.It offers a pretty straightforward approach to the common problem of sentence similarity. Named MaLSTM ("Ma" for Manhattan distance), its architecture is depicted in figure 1 (diagram excludes the sentence preprocessing part). Notice that since this is a Siamese network, it is easier to train because it shares weights on both sides.
A team of scientists in the United Kingdom and the U.S. recently reported the discovery of pathological signs of Alzheimer's disease in dolphins, animals whose brains are similar in many ways to those of humans. This is the first time that these signs – neurofibrillary tangles and two kinds of protein clusters called plaques – have been discovered together in marine mammals. As neuroscience researchers, we believe this discovery has added significance because of the similarities between dolphin brains and human brains. The new finding in dolphins supports the research team's hypothesis that two factors conspire to raise the risk of developing Alzheimer's disease in dolphins. Those factors are: longevity with a long post-fertility life span – that is, a species living, on average, many years after the child-bearing years are over – and insulin signaling.
Ask, and it shall be given you; seek, and ye shall find; knock, and it shall be opened unto you." Verse 7:7 from the Gospel of Matthew is generally considered to be a comment on prayer, but it could just as well be about the power of search. Search has become one of the key technologies of the information age, powering industry behemoths and helping us with our daily chores. But that's not where it ends. Scientists are starting to understand that search powers much of the natural world, too.
What happens when a tech artist and her gene-scientist husband try to wow the crowd at a "Nerd Nite" event in Kendall Square? They pitch an idea for an app to help fight disease by crowd-sourcing millions of 3-D digital maps of human faces. Facetopo was the brainchild of Boston documentarian and artist Alberta Chu and her husband Murray Robinson, whose brother was diagnosed with a rare disease that, like Down's syndrome, can be detected in the face. In a Q&A with Patch, Chu says some day participants could "maybe trade pictures, or eventually, find a twin." "Every user who wants to participate creates a private account and is able to download the app on either IOS or Android where we provide instructions so that you can create a 3-D face map.
Regular articles on Artificial Intelligence (AI), Machine Learning and Deep Learning appear in the media. Some commentators use these terms synonymously. However, although AI, machine learning, and deep learning are often closely intertwined, they are based on completely different technologies and have their unique attributes. Artificial Intelligence – sounds quite futuristic or even science fiction, this is because this topic has been appearing in the media for over 60 years. Until recently, however, we lacked the necessary prerequisites to apply the resources required for complex AI algorithms completely.
So it takes a snippet of speech and then translates the snippet of speech using the voice style of another person. The surprising point though of this research is that its able to encode an internal representation of a speech absent the speaking style. Of course, that sounds like voice to text translation. Now it somehow is able to take out a speech style and transpose it elsewhere. The approach in the paper uses auto-regressive networks, one of those curiously strange thingamajigs that DeepMind seems to be enamored with.
We first removed irrelevant tweets. A step we were able to take thanks to our "Small Data" situation. We then used Jeremy Singer-Vine's markovify -- a Markov chains implementation -- to model Netanyahu's original tweets. That alone actually gave us a pretty good baseline, in a very short time. We also expanded the Markov model to obey sentence structure using spaCy, a part-of-speech tagger.
There's a great deal of misunderstanding and misinformation around what computers can and can't do. Sadly, while Artificial Intelligence might not be as thrilling as a mid year blockbuster, it's similarly as energizing in the market research industry. A quick instruction on the difference between data mining, Artificial Intelligence, and machine learning (and how they work together) can give you a fundamental comprehension of why they're the genuine stars of market research, and, if utilized together, can exhibit an impressive strategy that one can use to overcome any information question or problem. Data mining is really one of the most recent strategies that market researching organizations are utilizing, yet it fills in as a foundation for both Artificial Intelligence and machine learning. Data mining, as a practice, is something other than dissecting supersets of data from different sources.
Organizations today deal with huge amount and wide variety of data – calls from customers, their emails, tweets, data from mobile applications and what not. It takes a lot of effort and time to make this data useful. One of the core skills in extracting information from text data is Natural Language Processing (NLP). Natural Language Processing (NLP) is the art and science which helps us extract information from text and use it in our computations and algorithms. Given then increase in content on internet and social media, it is one of the must have still for all data scientists out there.
Social media provide a low-cost alternative source for public health surveillance and health-related classification plays an important role to identify useful information. We summarized the recent classification methods using social media in public health. These methods rely on bag-of-words (BOW) model and have difficulty grasping the semantic meaning of texts. Unlike these methods, we present a word embedding based clustering method. Word embedding is one of the strongest trends in Natural Language Processing (NLP) at this moment.