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What businesses need to understand about chatbots
Liraz Margalit serves as customer experience psychologist for Clicktale, which helps businesses optimize visitor interactions with their websites. Welcome to the bot-centric future, which is set to make smartphone users -- i.e. almost everyone in the Western hemisphere -- navigate the internet in a chit-chat fashion with a virtual assistant. But "assistant" will soon become too impersonal… Alexa, Siri and others will cross the line from impersonal robots to entities that know our habits, routines, hobbies and interests just as well as, if not better than, our closest friends and relatives. For companies, this is a winning formula: Smartphone users have proved they are only willing to download and spend time in a limited number of apps. As such, businesses might be better off trying to connect with consumers in the apps where they are already spending plenty of time.
How much security can you turn over to AI?
It's not always easy to know when you're under attack, or when your security has already been breached. If you're capable of detecting a breach, you might find it in as few as 10 days, but survey after survey finds that breaches that are detected by someone outside the business typically take over 100 days to find. For one thing, between ecommerce, company websites, email, mobile users and overseas divisions, your company is doing business 24/7; however, your IT security team probably works business hours. That's one way 60 percent of attackers are able to compromise an organization in minutes, according to Verizon's 2015 Data Breach Investigations Report. But only a third of businesses can detect a breach within a few days.
Cannes Lions 2016: Key trends - JWT Intelligence
Cannes Lions this year saw the ad industry expanding its creative capabilities. Over 13,500 delegates from about 90 countries descended on Cannes again this year hoping for a Lion in one of 17 categories. With awards honoring work from design to creative data to radio, the ceremonies reflected a complex industry drawing on a broader range of creative disciplines than in the past, but also facing unprecedented challenges in making campaigns work across channels. "There's never been so many channels or points of interactions, or agencies working on various parts of that," said Keith Weed, chief marketing and communications officer at Unilever. "It's important to make sure the brand experience does not get fragmented."
Echobox, the AI for publishers, raises 3.4 million
Echobox, a London-based AI startup targeting the publishing industry, has secured 3.4 million from Mangrove Capital Partners and LocalGlobe. The startup's AI service is used by publishers to enhance their reach and distribution on social media. The startup claims its tech can predict if an article will go viral and when the optimal time to publish content is. Axel Springer and Le Monde are among its clients. "Having worked at a well-known publisher, I was amazed by how they were investing vast resources in trying to perfect the science of content distribution in-house with data scientists and analytics tools to guide decision-making," said Echobox CEO Antoine Amann.
Reports Confirm Upcoming Boom in AI Systems in Cars
Artificial intelligence is being used all around us and in the automotive market especially. Companies are fighting it out to develop the next revolutionary product featured around autonomous driving. This is already becoming apparent with the rise in the number of artificial intelligence (A.I) devices being used throughout various industries. The IHS, who are a company specializing in producing reports and statistics, have been conducting reports based upon this field, and the numbers are quite staggering. They have projected that the use of A.I based systems will rocket from 7 million (2015 figure) to 122 million by 2025, with over 100% (some cars will have multiple A.I devices) of them being installed in new vehicles.
Python Machine Learning Cookbook PACKT Books
Prateek Joshi is an Artificial Intelligence researcher and a published author. He has over eight years of experience in this field with a primary focus on content-based analysis and deep learning. He has written two books on Computer Vision and Machine Learning. His work in this field has resulted in multiple patents, tech demos, and research papers at major IEEE conferences. People from all over the world visit his blog, and he has received more than a million page views from over 200 countries.
Python Machine Learning PACKT Books
Machine learning is transforming the way businesses operate. Being able to understand trends and patterns in complex data is critical to success; it is today one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of machine learning and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable.
A Beginner's Tutorial for Restricted Boltzmann Machines - Deeplearning4j: Open-source, distributed deep learning for the JVM
Invented by Geoff Hinton, a Restricted Boltzmann machine is an algorithm useful for dimensionality reduction, classification, regression, collaborative filtering, feature learning and topic modeling. Given their relative simplicity and historical importance, restricted Boltzmann machines are the first neural network we'll tackle. In the paragraphs below, we describe in diagrams and plain language how they work. RBMs are shallow, two-layer neural nets that constitute the building blocks of deep-belief networks. The first layer of the RBM is called the visible, or input, layer, and the second is the hidden layer. Each circle in the graph above represents a neuron-like unit called a node, and nodes are simply where calculations take place.
Don't call them chatbots, call them intelligent assistants
Unless you've been hiding under a rock, you've no doubt heard about chatbots. However, what's not commonly known is that chatbots have been around for years. What differentiates today's bots is the integration of back-end artificial intelligence, enabling them to do more than simply respond with the basic logic of yesterday. I find it's important to make distinctions between a bot and today's A.I.-powered intelligent assistants. Chatbots just happen to be conversational.