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) …
Ahead of Amazon's big AWS division Re:invent conference next week, the company has announced two developments in the area of artificial intelligence. AWS is opening a machine learning lab, ML Solutions Lab, to pair Amazon machine learning experts with customers looking to build solutions using the AI tech. And it's releasing new features within Amazon Rekognition, Amazon's deep learning-based image recognition platform: real-time face recognition and the ability to recognize text in images. The new lab and the enhancements to its image recognition platform underscore the push that Amazon and AWS are giving to AI at the company, both internally and as a potential area to grow its B2B business in this area. They come about a month after AWS announced it would be collaborating with Microsoft on Gluon, a deep learning interface designed for developers to build and run machine learning models for their apps and other services.
French startup Snips is now helping you build a custom voice assistant for your device. Snips doesn't use Amazon's Alexa Voice Service or Google Assistant SDK -- the company is building its own voice assistant so that you can embed it on your devices. And the best part is that it doesn't send anything to the cloud as it works offline. If you want to understand how a voice assistant works, you can split it into multiple parts. First, it starts with a wakeword.
If you're not using deep learning already, you should be. That was the message from legendary Google engineer Jeff Dean at the end of his keynote earlier this year at a conference on web search and data mining. Dean was referring to the rapid increase in machine learning algorithms' accuracy, driven by recent progress in deep learning, and the still untapped potential of these improved algorithms to change the world we live in and the products we build. But breakthroughs in deep learning aren't the only reason this is a big moment for machine learning. Just as important is that over the last five years, machine learning has become far more accessible to nonexperts, opening up access to a vast group of people.
Key Points: – Businesses often overlook important issues related to morals and ethics of chatbots and AI – Customers need to know when they are communicating with a machine and not an actual human – Ownership of information shared with a bot is another key ethical consideration and can create intellectual property issues – The privacy and protection of user data is paramount in today's interconnected world You can also listen to The Modern Customer Podcast with Rob High here.) Businesses are rapidly waking up to the need for chatbots and other self-service technology. From automating basic communications and customer service, to reducing call center costs and providing a platform for conversational commerce, chatots offer many new opportunities to delight and better serve consumers. Chatbots can offer 24/7 customer service, rapidly engaging users, answering their queries as whenever they arrive. Millennials in particular are impatient when engaging with brands and expect real-time responses.
The age of Big Data has reached an all new high as disruptive and innovative digital technologies push businesses to adapt quickly in a rapidly changing consumer market. The capabilities and agility of big data combined with the scale of artificial intelligence is helping businesses across industries to understand evolving consumer behaviour and preferences, gain business intelligence and apply valuable insights when creating strategies. The convergence of big data and AI is the most significant development for businesses across the globe, enabling them to capitalise on hitherto unexplored opportunities. A major factor accentuating the importance of big data is also the massive volume of, and speed at which data is created through digital technologies and devices, providing businesses with real-time access to information from far more number of sources than ever before. As the driving force of several industries in 2017, the scale and growth of artificial intelligence in 2018 is expected to be even more greater.
If you have ever played a video game, no matter what era you played it in, you have interacted with artificial intelligence. Regardless of whether you prefer race-car games like Gran Turismo, strategy games like God Of War, or shooting games like Call Of Duty, you will always find elements controlled by AI. Even things that you don't think would be AI controlled, are! AIs are often behind the characters you typically don't pay much attention to, such as enemy creeps, neutral merchants, or even animals and other background characters. When it comes to video games, artificial intelligence has grown leaps and bounds, allowing us to have some of the most realistic gameplay experiences yet.
AI, to me is the magic that transpires when large amounts of data are refined and compartmentalised by high speed, computing power, with an end of delivering highly personalised human experiences. The advent of this super power, for all its indisputable potential, has induced fear in some; excitement in others but most definitely inspired a collective sentiment of curiosity. Some of the key topics that have snuck into daily discourse have questioned the capability of man and machine to work together in higher synergy, more interesting has been our paranoia towards an antagonism that could emerge between "them" and us. Could AI create a utopian work environment where time is fully taken away from mundane and repetitive tasks? Could this leave us to spend 100% of our time on tasks that require our passion, creativity and qualities that fundamentally make us human?
In our daily lives we are all faced with the need to make decisions. We usually make personal decisions based on personal experience and information. We draw from our friends' experience as well as Internet and other external sources. The data needed to solve a particular challenge usually fits in our head and is structured in sketches in our mind, a notebook or a special document such as mind map. In business, the volume of available data increases in multiples and we are tasked with data collection, analysis and the sheer impossibility of not only holding all the information in our head but even structuring it in a single document.
In the context of machine learning, tensor refers to the multidimensional array used in the mathematical models that describe neural networks. In other words, a tensor is usually a higher-dimension generalization of a matrix or a vector. Through a simple notation that uses a rank to show the number of dimensions, tensors allow the representation of complex n-dimensional vectors and hyper-shapes as n-dimensional arrays. Tensors have two properties: a datatype and a shape. TensorFlow is an open source deep learning framework that was released in late 2015 under the Apache 2.0 license.
Gadgets are only as good as their content, and though 2017 has been a difficult year for the world, it's been a great one for video games. As gaming elbows its way to the centre stage of mainstream culture, the titles and their themes are increasingly reflecting the wide variety of players and their concerns. Here are the best games and consoles, and the most exciting trends of 2017. Physical disabilities are rarely seen or catered for in games, but Xbox has addressed both issues. The new Co-pilot feature is useful for those using a controller who struggle with actions that are physically difficult.