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) …
Sometimes rather than aim for the grand plan, make something simple and fun to show it works first. Anyone starting a data science project is often excited about the potential and will reach for the stars, before you know it you've a horrendously ambitious and complicated project and you don't know where to start. The result is you never start it because you never get sufficient of the "hooks" done. Note: If you want to get straight to the machine learning project then feel free to skip ahead. Just as you need a fish to bite the hook so you can be a successful fisher, these are things you need to get a bite on before you think you can make a success of something.
Data science or applied statistics courses typically start with linear models, but in its way, K-nearest neighbors is probably the simplest widely used model conceptually. KNN models are really just technical implementations of a common intuition, that things that share similar features tend to be, well, similar. This is hardly a deep insight, yet these practical implementations can be extremely powerful, and, crucially for someone approaching an unknown dataset, can handle non-linearities without any complicated data-engineering or model set up. As an illustrative example, let's consider the simplest case of using a KNN model as a classifier. Let's say you have data points that fall into one of three classes.
Many companies rush to operationalize AI models that are neither understood nor auditable in the race to build predictive models as quickly as possible with open source tools that many users don't fully understand. In my data science organization, we use two techniques -- blockchain and explainable latent features -- that dramatically improve the explainability of the AI models we build. In 2018 I produced a patent application (16/128,359 USA) around using blockchain to ensure that all of the decisions made about a machine learning model, a fundamental component of many AI solutions, are recorded and auditable. My patent describes how to codify analytic and machine learning model development using blockchain technology to associate a chain of entities, work tasks and requirements with a model, including testing and validation checks. The blockchain substantiate a trail of decision-making.
GE Healthcare officially launched the Edison AI platform in Shanghai, China at its Digital Ecosystem Forum event. GE also signed a Memorandum of Understanding (MoU) of strategic partnership with five local software development companies: Shukun Technology, Yizhun Medical AI, YITU Technology, 12Sigma Technologies and Biomind. Under the MoU, GE will cooperate with the five software vendors to develop the platform's applications in China. GE Healthcare's Edison platform was first introduced at last year's Radiological Society of North America annual meeting in Chicago in November. The platform is touted as a way to help hospitals derive more value from their technology.
Customer queries are the bane of most customer support teams, not because they don't like dealing with them, but because they don't have a proper process in place that lets them handle excessive ticket volumes easily and effectively. When a support ticket drops into a queue, or an agent receives an email with a customer issue, the ticket or email might pass through three different agents before finally landing in the correct hands to deal with the issue – leading to bottlenecks and bad customer experiences. Bugs, forgotten passwords, system errors, integration queries… There are so many different issues that agents have to deal with, so that the customer remains happy and the company retains them. And while customer support endeavors to respond to queries as quickly as possible, it's difficult when faced with huge volumes of tickets. On top of that, more and more customers expect immediate responses – 64% of consumers and 80% of business buyers said they expect companies to respond to and interact with them in real time.
It has been abundantly clear for quite some time that enterprise technology development has been focused on the digital workplace, but according to a recent book from Tom Seibel, which we discussed last month, what is happening now is a game changer. In Digital Transformation: Survive and Thrive in an Era of Mass Extinction, he argues that technology is at a inflection point and that the principle technology discussion in the digital workplace at the moment is how to manage the convergence of four megatrends: cloud computing, big data, artificial intelligence (AI) and Internet of Things (IoT). While these systems are making work more'intelligent' they are also increasingly difficult to manage. The manufacturing industry, for example, has been working with these trends separately for years in a number of different ways, according to Maryanne Steidinger of Webalo, and they are all starting to dovetail through the use of data. Here's how each technology is working in the enterprise: Software is now being offered as a service (i.e., cloud-based, where you are essentially leasing vs. purchasing it) for the past 10 years.
The turbulence of Brexit has left both UK and European startups alike wondering about the best path forward. From recruiting to acquiring investment to scaling into other parts of Europe, the challenges seem to be mounting. By December, who knows what will have happened on the Brexit landscape, such is the chaos. At Disrupt Berlin in December, we'll hear from investor Bindi Karia who has deep European ties, founder Glenn Shoosmith who's expanding his startup internationally and German-born but UK-domiciled VC Volker Hirsch on how to make the right decisions in the face of these obstacles. Bindi Karia works as a venture partner at a large london-based VC and has held positions in and around the tech industry for as long as she's been working.
Study: Intelligence AI chatbots are four times more effective at selling products than inexperienced humans. First impressions make a big difference in business. It turns out that's not only the case for human-to-human encounters, but also for interactions with chatbots. Artificial intelligence (AI) chatbots are four times more effective at selling products than inexperienced workers, according to a new study. However, if customers know the conversational partner is not a human, they are curt and purchase less.
Artificial intelligence (AI) is one of the most exciting technologies in the world right now. In particular, it's bringing life to ideas that were once just a figment of Hollywood films. However, it has also created polarised viewpoints. Many AI experts are working towards reaping its full potential, while others worry about creating a Black Mirror-esque reality. Perhaps the best way to meet in the middle is by exploring explainable AI.