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) …
But 83% are unaware of how to deploy artificial intelligence & machine learning to address specific business problems, so much benefit is still to be realised Zurich, London, 18 April 2018 – The use of artificial intelligence (AI) and machine learning (ML) in financial services (FS) is on the rise, with 83% of banks having evaluated AI & ML solutions, and 67% having actively deployed them, according to a new study out today. The research with 200 global tier one and tier two banks was conducted by capital market research firm TABB Group on behalf of augmented intelligence solutions provider Squirro, and revealed that AI is the most important'disrupter' for banks today. The study – 'Enhanced Bankers – The Impact of AI'- also highlighted a lack of understanding around AI & ML as specifically applied to improving business processes, with 83% of respondents still unaware of how to apply the technology to solve business problems. Using AI and machine learning to source new leads and opportunities is key to bankers, with 87% of respondents saying that it would be highly impactful if an AI engine could spot relevant events that led to engaging with a client and closing a deal. Bankers recognize that AI driven insights will have a tremendous impact when it comes to anticipate market events to stay ahead of the competition.
The UK is in a strong position to be a world leader in the development of artificial intelligence (AI) – but an ethical approach to doing so will be central to future success, says a new House of Lords report. Machine-learning, the precursor to AI, is already widely found across retail, in use in tools from chatbots to recommendations. Retailers from Shop Direct to the Yoox Net-A-Porter Group and Ocado are investing heavily in the relevant technologies which are set to lead to true AI in time. Machine learning automates human decision making, enabling it to happen at scale. Retailers will move further towards true AI when they develop machines that can make original decisions, reaching altogether new conclusions.
If you've bought anything online recently, chances are you've experienced something like this: You visit a store's website to buy a sci-fi movie. The website makes recommendations of other movies for you to purchase. The next day, you get a follow-up email recommending other similar movies, and even some similar books. Intrigued by one of the books on the list, you decide to buy it as well. All of those recommendations are powered by artificial intelligence.
From machine learning applications in retail, to intelligent systems in e-commerce, artificial intelligence (AI) is completely changing the digital landscape. Digital advertising is becoming smarter with improved targeting, the digital shopping experience is getting revitalized with tailored recommendations and streamlined transaction, and AI is the catalyst in the middle leading the charge. On the business side, AI drives revenue growth by allowing us to engage existing customers in more meaningful ways. Through this, targeting and segmenting is also streamlined, something that benefits consumers as well because they see more of what they want, rather than what the retailer wants. The point being that AI is improving the digital shopping experience for all.
The largest AI personal styling solution, Epytom, announced at the Vanity Fair's Founders Fair, the launch of their AI-designed made-to-order clothing line available in July 2018. Offering the only customer-centric alternative to the fast fashion model Epytom, founded in 2016, is a pioneer in low-cost, scalable fashion personalisation. Epytom is a free personalised "what-to-wear" recommendation AI styling solution. They offer their three hundred thousand monthly active users a unique service based on likes and dislikes, wardrobe insights, style preferences, and body parameters. This shared information allows Epytom to create clothing pieces for you and you alone.
IRI Demand Forecasting, to help marketing, finance, and sales teams identify opportunities to drive sales growth. IRI Revenue Management, to help users optimize and track price and promotion strategies. A new Price Recommendation Engine leverages machine learning to evaluate products, competitors, retailers, and geographies and subsequently identify pricing and promotion opportunities. IRI Assortment Optimization now helps users leverage machine learning to proactively look across retailers and products to alert opportunities. Patterns of incremental sales potential are prescriptively evaluated across millions of possible improvements.
"Data Scientist is the sexiest job of the 21st century" – Harvard Business Review "Expect a shortage of over 100,000 data scientists by 2020" – Gartner Unarguably, in today's hyper-competitive marketplace, Data Science plays an indispensable role for organizations to personalize experiences and create value out of their data. Analyzing large data sets without preset defined rules or scope for analysis to uncover insights, a sublime concept till a few years ago, will form the key basis of competition in the future to significantly unlock business value, unleashing new waves of productivity for businesses, enabling a culture of innovation, and reinvigorating internal processes, as long as the right ecosystem and enablers are put in place. Numerous articles today are buzzing with this glamourous new word in the Analytics world i.e. So what exactly is Data Science or this hype around Data Scientist? Frankly speaking, multiple definitions, roles, job descriptions exist making it harder for businesses to understand what truly is the role about and the ROI out of making any additional investments.
Machine learning and AI are ubiquitous these days, and people are finding all kinds of creative ways to use it in the realm of research. Here are a few ways Elsevier and our innovative collaborators are using it. Big data and machine learning platforms can also do more than fuel recommendation engines. They can become part of the scientific discourse by performing a task they're particularly suited to – scanning vast amounts of information to unearth connections that lead to new hypotheses. A pilot program between Elsevier and the Euretos AI platform aims to use these technologies to scan millions of journal articles and hundreds of databases to make connections and suggest new hypotheses for researchers to investigate.
Look at how far we've come with AI and healthcare. Not long ago, I took a look at the telehealth scene and noted that there were no algorithms coming up with direct diagnoses of illnesses. There's Dr. Google, a symptom checker that taps data from the Mayo Clinic and Harvard Medical School and uses Google's AI to make recommendations. Alexa can now deliver "first aid" information through the Mayo Clinic's First Aid Alexa Skill. Additionally, WebMD has an integration that basically does what WebMD already does, just through Alexa, while Dr. A.I. by HealthTap uses machine learning to make recommendations based what doctors have previously suggested to people with similar symptoms.
Artificial intelligence is revolutionizing businesses across industries. More than half of the executives surveyed in a 2017 PwC report said that AI solutions were already increasing their companies' productivity. As usual, marketers are at the forefront, embracing AI at a particularly rapid pace. But while any new resource can create excitement in some, it can make others feel uncertain--sometimes even worried about their futures. Many marketers fear that onboarding AI will fundamentally change the way they do business, and not completely for the better.