Machine learning is rapidly having a big impact on ecommerce as martech and agencies invest in data science. Off the back of this, performance marketing agency iProspect got in touch to tell me about their work with keyword categorisation. I asked a few questions of Owned Media Executive Josh Carty, to find out more. Before we get stuck in, a reminder that this year's Festival of Marketing features one stage (of 12) dedicated to AI in marketing – book your tickets now and see headliners such as Stephen Fry and Jo Malone. Josh Carty: We work with one of the UK's largest online retailers on their organic search and performance content strategy.

In supply chain management, globalization, geopolitical forces and increased demand for greater transparency have organizations looking at cutting-edge technology to boost performance and cut risk. Analysts have been discussing some of these technologies for some time. Blockchain, for example, is now a common topic in conversations surrounding supply chain innovation. But here's a technology you may not have expected to also be an up-and-coming focus for the supply chain: chatbots. In corporate finance, chatbots are gaining steam in areas like banking and accounting.

The technology, known as SharkSpotter, uses an algorithm to detect sharks in a live video feed recorded in real time by a drone (known as the Little Ripper Livesaver) flying above the water. Using a world-first algorithm, developed using artificial intelligence and deep neural networks, SharkSpotter is able to distinguish sharks from dolphins, rays and other marine animals, and even surfers. Thanks to an onboard megaphone, the drone can also warn swimmers about what's lurking in the water before they've even seen the threat. Watch the video here: What do you think about this. Let us know in the comments.

AI and ML are often used interchangeably, but they are in fact, not the same. Take a deep dive to learn more about these tech buzzwords! Artificial Intelligence (AI) and Machine Learning (ML) are some of the hottest buzzwords in tech these days, and maybe you want in. But first, let's take a moment to distinguish the two because while they may often be used interchangeably, they are in fact, not the same. AI is the concept of machines being able to carry out tasks in ways that humans would consider "smart."

Tree based learning algorithms are considered to be one of the best and mostly used supervised learning methods. Tree based methods empower predictive models with high accuracy, stability and ease of interpretation. Unlike linear models, they map non-linear relationships quite well. They are adaptable at solving any kind of problem at hand (classification or regression). Methods like decision trees, random forest, gradient boosting are being popularly used in all kinds of data science problems. Hence, for every analyst (fresher also), it's important to learn these algorithms and use them for modeling. This tutorial is meant to help beginners learn tree based modeling from scratch. After the successful completion of this tutorial, one is expected to become proficient at using tree based algorithms and build predictive models. Note: This tutorial requires no prior knowledge of machine learning.

RepuGen is an online reputation management platform for the healthcare industry which enables doctors to monitor and improve patient satisfaction through machine learning driven analytics and reporting. RepuGen filters out negative reviews even before they are posted online allowing healthcare providers time to react, address the situation and improve patient experience. Q: What is RepuGen's mission? A: RepuGen's mission is to provide a seamless and effective solution to nurture patient relationships, improve online reputation, and attract new patients to a business. We believe that positive external reputation and internal relationships are the key to driving new business, especially in the healthcare and dental industry.

There didn't seem to be anything strange about the new teaching assistant, Jill Watson, who messaged students about assignments and due dates in professor Ashok Goel's artificial intelligence class at the Georgia Institute of Technology. Her responses were brief but informative, and it wasn't until the semester ended that the students learned Jill wasn't actually a "she" at all, let alone a human being. Jill was a chatbot, built by Goel to help lighten the load on his eight other human TAs. "We thought that if an AI TA would automatically answer routine questions that typically have crisp answers, then the (human) teaching staff could engage the students on the more open-ended questions," Goel told Digital Trends. "It is only later that we became motivated by the goal of building human-like AI TAs so that the students cannot easily tell the difference between human and AI TAs. Now we are interested in building AI TAs that enhance student engagement, retention, performance, and learning."

The buzz over artificial intelligence (AI) has grown loud enough to penetrate the C-suites of organizations around the world, and for good reason. Investment in AI is growing and is increasingly coming from organizations outside the tech space. And AI success stories are becoming more numerous and diverse, from Amazon reaping operational efficiencies using its AI-powered Kiva warehouse robots, to GE keeping its industrial equipment running by leveraging AI for predictive maintenance. While it's clear that CEOs need to consider AI's business implications, the technology's nascence in business settings makes it less clear how to profitably employ it. Through a study of AI that included a survey of 3,073 executives and 160 case studies across 14 sectors and 10 countries, and through a separate digital research program, we have identified 10 key insights CEOs need to know to embark on a successful AI journey.

The 100 startups on our list have raised $3.8B in aggregate funding across 263 deals since 2012. CB Insights unveiled the AI 100--a list of 100 of the most promising private companies applying artificial intelligence algorithms across industries, from healthcare to auto to fintech--at the Innovation Summit today. The companies were selected from a pool of nearly 500 applicants and nominees based on several criteria, including data submitted by the companies, responses to interview questions, technology focus, investor profile, team profile, mosaic scores, and funding history. "From financial services to healthcare to transport, incumbent companies in every industry are seeing that AI will reshape their industries. And as so often happens, transformational innovation comes from emerging companies. In the case of AI, a lot of the groundbreaking work is being done by the AI 100. The companies in the AI 100 are accelerating research, improving efficiency, and making many game-changing advancements that will be felt for decades to come," CB Insights CEO Anand Sanwal said in a press release.

In a recent Infosys multi-market research study, 'Amplifying Human Potential Toward Purposeful AI', pharmaceutical and life sciences emerged as the leader in the journey to AI maturity. For several years now, the pharmaceutical sector has been quietly using AI and automation technologies for clinical trials, drug analysis, and to accelerate the process of launching products. Artificial intelligence is also making a huge contribution beyond just making operations more efficient. One example is improving melanoma survival rates by identifying fatal blotches much earlier and more accurately by using technology to amplify human capability. Detecting this deadly cancer early makes a huge difference--survival rates can go as high as 98 percent from as low as 16 percent if detection happens before the disease progresses to the lymph nodes. Today, the Translational Genomics Research Institute's Center for Rare Childhood Disorders is running extremely complex algorithms to analyze massive quantities of genetic and molecular data to diagnose medical conditions and develop personalized treatments.