Since Klaus Schwab drew attention to the arrival of the 4th Industrial Revolution in January 2016, we are witnessing the significant impact of Artificial Intelligence and its more well known subset, often seen as its synonym of Machine Learning that led Andrew Ng to propound " AI is the new electricity". India created the IITs in the 1960s that helped some Indians to achieve places of eminence in the technical and business world of the 3rd Industrial Age. Today, India is a ' KRANTI' nation poised to play center-stage in the AI age leveraging the opportunity as a ' learning movement' amongst its masses, evidenced by the rapid adoption of the mobile phone. A group of former IIT faculty and students led by Prof. M.M. Pant have started a movement of AI awareness and lifelong learning to spread knowledge about AI as per one's need. Their Mission 2020 is to prepare not only the young but all ages for their future in the 4th Industrial Age.
For most businesses, machine learning seems close to rocket science, appearing expensive and talent demanding. And, if you're aiming at building another Netflix recommendation system, it really is. But the trend of making everything-as-a-service has affected this sophisticated sphere, too. You can jump-start an ML initiative without much investment, which would be the right move if you are new to data science and just want to grab the low hanging fruit. One of ML's most inspiring stories is the one about a Japanese farmer who decided to sort cucumbers automatically to help his parents with this painstaking operation. Unlike the stories that abound about large enterprises, the guy had neither expertise in machine learning, nor a big budget. But he did manage to get familiar with TensorFlow and employed deep learning to recognize different classes of cucumbers. By using machine learning cloud services, you can start building your first working models, yielding valuable insights from predictions with a relatively small team. We've already discussed machine learning strategy. Now let's have a look at the best machine learning platforms on the market and consider some of the infrastructural decisions to be made.
You might be familiar with structured data, it is everywhere. Here i would like to focus on discussion on how we transform unstructured data to something data machine can process the data then to take inference. As the time goes by, people think how to handle unstructured like text, image, data satellite, audio, etc. Tthat might give you something useful to make decision in your business. In this case i take from kaggle competition named What's Cooking. The competition wants you to classify type of food based on its ingredients.
As companies increasingly turn to AI and machine learning, a clearer picture of what it takes to succeed with real-world AI is beginning to take shape. Beyond the small circle of tech giants and early adopters, a different set of skills and approaches is emerging as must-haves for enterprise AI teams. Not every organization can compete with the likes of Google and Facebook for top AI talent. And it's not just data science PhDs that companies are looking for. To meet their business needs, CIOs assembling AI teams are looking for subject matter expertise, software engineering skills, and the ability to translate learning algorithms into actual business value.
Critics of the current mode of artificial intelligence technology have grown louder in the last couple of years, and this week, Google, one of the biggest commercial beneficiaries of the current vogue, offered a response, if, perhaps, not an answer, to the critics. In a paper published by the Google Brain and the Deep Mind units of Google, researchers address shortcomings of the field and offer some techniques they hope will bring machine learning farther along the path to what would be "artificial general intelligence," something more like human reasoning. The research acknowledges that current "deep learning" approaches to AI have failed to achieve the ability to even approach human cognitive skills. Without dumping all that's been achieved with things such as "convolutional neural networks," or CNNs, the shining success of machine learning, they propose ways to impart broader reasoning skills. The paper, "Relational inductive biases, deep learning, and graph networks," posted on the arXiv pre-print service, is authored by Peter W. Battaglia of Google's DeepMind unit, along with colleagues from Google Brain, MIT, and the University of Edinburgh.
MIT's move signals two trends in higher education: growing investment in sophisticated technology research and increased fundraising from the private sector. Last year, the university announced plans to partner with IBM on a 10-year, $240 million AI research effort. The resulting MIT-IBM Watson AI Lab is co-located with an IBM research facility in Boston and brings together faculty members and students as well as IBM and university researchers to enhance AI's impact across industries. More recently, IBM partnered with Columbia University to develop research competency in blockchain technology through the Columbia-IBM Center for Blockchain and Data Transparency. Public institutions are nabbing corporate funding, too.
ESILV engineering students, used to working on real-life cases, put their specific skills at the service of cross-disciplinary teams. Organised by ESILV, French business school IESEG and De Vinci FabLab, this one-day hackathon had over a hundred students from various higher education institutions work together: ESILV, IESEG, Epitech, Institut Mines Télécom and École Polytechnique. Organised in cross-disciplinary teams mixing business schools and engineering schools, the students had a few hours only to present a solution at the end of the day. Throughout the whole process, all teams were supported by professional coaches from Oracle, Accenture, Total, Orange, Cap Gemini to name but a few. In the late afternoon, teams pitched their solutions for each issue in front of jurys.
Neural networks can be sensitive to the starting point (i.e. Similar behavior is observed with random forest models due to random effects in searching the space for the model. The selection of folds can also introduce variations from one set of runs to another, if the folds vary. Generally, it is advised to take the folds in a uniform way stepping through the data. In most tutorials, you are advised to fix the "seed" for the random number generator in your programming language to avoid variations when trying to repeat runs.
Deep reinforcement learning(DRL) has been categorized many times as the future of artificial intelligence(AI). Some of the most important AI breakthroughs of the last few years such as DeepMind's AlphaGo or OpenAI's Dota Five have been based on DRL applications. Despite its importance, the implementation of DRL models remains an incredibly challenging exercise and, for the most part, we have very little ideas about the pieces that make an efficient DRL solution. Earlier this week, DeepMind open sourced TRFL(pronounced truffle, of course), a framework that compiles a series of useful building blocks of DRL models. Most of the current wave of DRL methods have had their origin in the academic environments and they haven't been tested in real world implementations.