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) …
Japanese researchers have built a robot with brain-like neurons that were grown in the lab, in order to teach it to'think like us'. In experiments at the University of Tokyo, the compact robotic vehicle on wheels, small enough to fit in a person's palm, was placed in a simple maze. The robot was connected to a culture of brain neurons, also known as nerve cells, that were grown from living cells. When these artificial neurons were electrically stimulated, the machine successfully reached its goal – a black circular box. A neuron, also known as nerve cell, is an electrically excitable cell that takes up, processes and transmits information through electrical and chemical signals.
A successful data scientist understands a wide range of Machine Learning algorithms and can explain the results to stakeholders. But, unfortunately, not every stakeholder has a sufficient amount of training to grasp the complexities of ML. Luckily, we can aid our explanations by using dimensionality reduction techniques to create visual representations of high dimensional data. This article will take you through one such technique called t-Distributed Stochastic Neighbor Embedding (t-SNE). Perfect categorization of Machine Learning techniques is not always possible due to the flexibility demonstrated by specific algorithms, making them useful when solving different problems (e.g., one can use k-NN for regression and classification).
It's a frustrating problem, given that it's so easy to solve. Those who embrace modern technology are already optimizing their inventory with advanced analytics, entirely preventing these massive amounts of overstock. So, if a retailer is wondering why consumers and investors are pulling away, it's because they are still using a traditional approach in a modern world. Whether it's fast fashion or high-end brands, at the end of the day, the goal of a business is to maximize shareholder value. As such, retailers can't afford to risk losing sales because they ran out of stock.
A. Lawfulness: AI applications will be developed and used in accordance with national and international law, including international humanitarian law and human rights law, as applicable. B. Responsibility and Accountability: AI applications will be developed and used with appropriate levels of judgment and care; clear human responsibility shall apply in order to ensure accountability. C. Explainability and Traceability: AI applications will be appropriately understandable and transparent, including through the use of review methodologies, sources, and procedures. This includes verification, assessment and validation mechanisms at either a NATO and/or national level. D. Reliability: AI applications will have explicit, well-defined use cases.
Pactera EDGE, a world-class digital solutions provider for the data-driven, intelligent enterprise, announced the appointment of Vasudevan Sundarababu as a Senior Vice President, Head of Digital Engineering. Sundarababu, who has over 25-years of IT industry experience, most recently served as Global Head of Cloud Data Platforms for Capgemini Financial Services. He was previously Chief Technology Officer of CSS Corp. In his new role, Sundarababu will lead Pactera EDGE's global digital engineering practice, where he will be responsible for the identification and design of new products and solutions, the development of technology strategies and capabilities, and the inception of programs to bring these opportunities to Pactera EDGE's clients. Additionally, he will provide support to the sales team for client proposals and solutions.
The collaboration of surgeons and AI algorithms must be regarded as a pair by blessing. AI-assisted surgeries have yielded significant attention of the surgeon on the patient's vital issue and solving it accurately. The use of AI in healthcare has diminished the time taken to manage peripheral circumstances. AI-assisted surgeries are the newest version of the update introduced in health tech. Making machines operate using their intelligence is a human quality imbibed in them with a massive infusion of algorithms that gives birth to artificial intelligence.
Artificial Intelligence is often viewed as a potential threat to humanity, but it can also be viewed as a valuable tool. From self-driving cars to automated security systems, AI is gradually becoming a valuable resource in our everyday lives. It's important to realize that AI can be used for good or bad purposes. One of the biggest risks of AI is that it is becoming more and more advanced, which is opening the door to the possibility of AI surpassing human intelligence. This is called the singularity, and it's something that should be considered because once it occurs, we may not be able to turn back.
When I first applied to Toptal, I wanted to become both a freelancer and a "real ML engineer" at the same time. Before that, I worked as a Machine Learning engineer at Nordeus, a top mobile gaming company famous for having Mourinho's face on its flagship game: TopEleven. My Machine Learning adventure at Nordeus consisted of designing and implementing an intelligent system to help the customer support team resolve player issues faster. The essence of it was to build a text classifier from a ton of historical player tickets and agent resolutions. I had the whole system in mind, the data (at least that is what I thought), and access to GPUs.
Expert.ai announced that its natural language (NL) API providing deep language understanding is now available in the AWS Marketplace, a digital catalog with thousands of software listings from independent software vendors that make it easy to find, test, buy, and deploy software that runs on Amazon Web Services (AWS). NL API is a powerful way to structure unstructured language data leveraging deep language intelligence with minimal effort. The API identifies which meaning of a word is used in context ("disambiguation") to quickly analyze text for key elements, relations, classifications and more. It can also determine sentiment and even capture a range of 117 behavioral and emotional traits, providing the richest, most comprehensive and granular emotional and behavioral taxonomy available throughout the AI-based API ecosystem. Furthermore, using built-in technologies and its extensive knowledge graph, the expert.ai NL API can be used in more targeted ways to identify sensitive data (to protect customers, victims, users or research subjects, as well as to comply with data privacy regulations), media-related topics, geographical taxonomies and more.
Overfitting in machine learning and deep learning is a common problem. This is a result of the model being too biased toward the data and not generalizing well. In this article we will go through a few common ways to mitigate overfitting. We will use some of the most common overfitting solutions which can be used for Python or R, with a full example run-through with Python and Tensorflow. The most controversial way to stop overfitting a model is to reduce its complexity.