"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Among the various companies, non-profits and researchers using tech company Google's TensorFlow platform, one application that has caught the attention of developers at the internet giant is PlantMD. Created by high school students Shaza Mehdi and Nile Ravenell, the app can detect diseases in plants. The duo, who showcased the app at Google's I/O annual developer conference this year, built it based on the Internet company's open-source machine learning library for data programming--TensorFlow. "PlantMD's machine learning model was inspired by a dataset from PlantVillage, a research and development unit at Penn State University. PlantVillage created an app called Nuru, Swahili for'light', to assist farmers to grow better cassava, a crop in Africa that provides food for over half a billion people daily," Fred Alcober, a member of Google's TensorFlow team, wrote in a blog post. Cassava plants, wrote Alcober, though very tolerant of harsh weather conditions, is susceptible to pests and diseases.
This article focuses on the political and geopolitical consequences of the feedback relationship linking Artificial Intelligence (AI) in its Deep Learning component and computing power – hardware – or rather high performance computing power (HPC). It builds on a first part where we explained and detailed this connection. There we underlined notably three typical phases where computation is required: creation of the AI program, training, and inference or production (usage). We showed that a quest for improvement across phases, and the overwhelming and determining importance of architecture design – which takes place during the creation phase – generates a crucial need for ever more powerful computing power. Meanwhile, we identified a feedback spiral between AI-DL and computing power, where more computing power allows for advances in terms of AI and where new AI and the need to optimize it demand more computing power.
Artificial intelligence has an unimaginable potential. Within the next couple of years, it will revolutionize every area of our life, including medicine. I am fully convinced that it will redesign healthcare completely – and for the better. Let's take a look at the promising solutions it offers. There are various thought leaders who believe that we are experiencing the Fourth Industrial Revolution, which is characterized by a range of new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human.
Artificial Intelligence(AI) refers to an advanced technological field where machines and robotic systems can replicate human thinking. Artificial Intelligence enables a robotic system to use its sensory and cognitive abilities for decision making purpose. In the Information technology-driven world of today, AI can play a significant role in widespread sectors. Therefore, it is expected to create new careers avenues for skilled individuals in the years to come. Artificial Intelligence is one of the most infomercial aspects of applied science and technology.
Machine learning (ML) in business is changing the world industry marketing platform effectively through its new advancements. Its impact on organizations shows that the future of marketing will comprise of savvy marketers working as a team with machine learning based automation entities. Machine learning technologies are used to find a solution for various problems and new ways to create advantages for the business. In the latest marketing strategy, machine learning is being used to find the predictive information in the form of structured and unstructured data and to use them for business growth. Machine learning in business is a powerful and proven concept that enables computer systems to make sense of things for themselves.
This paper addresses a key NLP problem known as sarcasm detection using a combination of models based on convolutional neural networks (CNNs). Detection of sarcasm is important in other areas such as affective computing and sentiment analysis because such expressions can flip the polarity of a sentence. Sarcasm can be considered as expressing a bitter gibe or taunt. Examples include statements such as "Is it time for your medication or mine?" and "I work 40 hours a week to be this poor". To understand and detect sarcasm it is important to understand the factsrelated to an event.
This post is authored by Viral B. Shah, co-creator of the Julia language and co-founder and CEO at Julia Computing, and Avik Sengupta, head of engineering at Julia Computing. The Julia language provides a fresh new approach to numerical computing, where there is no longer a compromise between performance and productivity. A high-level language that makes writing natural mathematical code easy, with runtime speeds approaching raw C, Julia has been used to model economic systems at the Federal Reserve, drive autonomous cars at University of California Berkeley, optimize the power grid, calculate solvency requirements for large insurance firms, model the US mortgage markets and map all the stars in the sky. It would be no surprise then that Julia is a natural fit in many areas of machine learning. And the powers of Julia make it a perfect language to implement these algorithms.
For the past month, we ranked nearly 250 Machine Learning Open Source Projects to pick the Top 10. We compared projects with new or major release during this period. Mybridge AI ranks projects based on a variety of factors to measure its quality for professionals. Open source projects can be useful for programmers. Hope you find an interesting project that inspires you.
These and many other fascinating insights were shared during Eightfold.ai's Panel participants included Monika Fahlbusch, Chief Employee Experience Officer at BMC Software and former Senior Vice President, Global Employee Success at Salesforce, Ciara Ennis, Senior Marketing Manager EMEA for Twilio, Russell Williams, former Vice President of Human Resources at PARC, A Xerox Company and Ashutosh Garg, Eightfold.ai The Honorable Ananth Kumar Hegde, Union Minister of State, India, also addressed in detail his plans for re-skilling India and empowering youth in his nation. The panel provided insights into how CHROs are meeting the many challenges of talent management today and provides valuable lessons learned. How Machine Learning Is Solving One Of HR's Greatest Paradoxes Winning the war for talent must be multidimensional with a strong focus on making every HR process more employee-centric.