THE COFFEESHOP is an engine of social mobility. Barista jobs require soft skills and little experience, making them a first port of call for young people and immigrants looking for work. So it may be worrying that robotic baristas are spreading. RC Coffee, which bills itself "Canada's first robotic café", opened in Toronto last summer. "[T]he barista-to-customer interaction is somewhat risky despite people's best efforts to maintain a safe environment," the firm says.
Limitations on physical interactions throughout the world have reshaped our lives and habits. And while the pandemic has been disrupting the majority of industries, e-commerce has been thriving. This article covers how reinforcement learning for dynamic pricing helps retailers refine their pricing strategies to increase profitability and boost customer engagement and loyalty. In dynamic pricing, we want an agent to set optimal prices based on market conditions. In terms of RL concepts, actions are all of the possible prices and states, market conditions, except for the current price of the product or service.
The Federal Reserve Board, the CFPB, the FDIC, the National Credit Union Administration and the OCC (the "agencies") solicited comment on financial institution use of artificial intelligence ("AI") and machine learning. The agencies are seeking information on operational purposes, governance and cybersecurity, risk management, credit decisions, and controls over AI, as well as whether the agencies can provide guidance regarding a financial institution's use of AI in a safe and sound manner. Comments on the request for information must be submitted within 60 days of its publication in the Federal Register.
Register for a free or VIP pass today. CrowdAI, a computer vision development platform, today announced that it closed a $6.25 million series A financing round led by Threshold Ventures. The fundraising coincides with the launch of the startup's new solution that allows customers to create AI that analyzes images and videos. The AI skills gap remains a significant impediment to adoption in most enterprises, a 2020 O'Reilly survey found. Slightly more than one-sixth of respondents cited difficulty in hiring experts as a barrier to AI deployment in their organizations.
From the business closure to economic turmoil, the year 2020 has disrupted every aspect of our lives. Many events, conferences and other meetings in technology have been cancelled or postponed. However, as many economic activities are coming to the new normal, many conferences are starting to take place both online and in person. Analytics Insight has listed here the top 10 upcoming AI and ML conferences that will help you decide which one to attend and which one suits you. With artificial intelligence and machine learning presenting new possibilities, AI and ML conferences are gaining much popularity.
A startup is employing machine learning to process aerial imagery and remotely analyze insurance risks to properties around the country. Why it matters: The combination of AI and aerial imagery from satellites and even stratospheric balloons can help insurers quickly judge property risks without an in-person visit, saving money and time. How it works: Arturo's AI model can identify potentially risky characteristics of a property -- like roof tiles in need of repair or a pool that lacks a fence -- and estimate the likelihood of an insurable accident in the future. Background: Arturo's business model is a combination of two major technological trends: the ever-increasing growth of aerial imagery that can capture detailed pictures of the ground and the power of machine learning. The big picture: Insurance might seem like the blandest of businesses, but since its origins hundreds of years ago, the field has focused on using available data to try to predict the future -- which happens to be precisely what machine learning is good at.
The world celebrated Women's History Month in March, and it is a timely moment for us to look at the forces that will shape gender parity in the future. Even as the pandemic accelerates digitization and the future of work, artificial intelligence (AI) stands out as a potentially helpful--or hurtful--tool in the equity agenda. McKinsey recorded a podcast in collaboration with Citi that dives into how gender bias is reflected in AI, why we must consciously debias our machine-human interfaces, and how AI can be a positive force for gender parity. Ioana Niculcea: Before we start the conversation, I think it's important for us to spend a moment assessing the amount of change that has taken place with regard to AI, and how the pace of that change has accelerated over the past few years. And many people argue that in light of the current COVID-19 circumstance, we'll feel further acceleration as people move toward digitization. I spent the past eight years in financial services, and it all started with data. Datafication of the industry was sort of the point of origin. And we hear often that over 90 percent of the data that we have today was created over the past two years. You hear things like every minute, there's over one million Facebook logins and 4.5 million YouTube videos being streamed, or 17,000 different Uber rides. There's a lot of data, and only 1 percent of that is being analyzed, as said today.
Chatbots will be more proactive, says Zor Gorelov, chief executive of Kasisto, a company creating conversational AI for banking and finance clients. They'll be able to anticipate individuals' needs and offer advice before users even ask a question, though there is still a long way to go before many of these features become a reality. Instead of pointing you to a resource such as a phone line or FAQ page, chatbots could one day be resources themselves, able to offer highly personalized responses to individual questions and scenarios. A look at how innovation and technology are transforming the way we live, work and play. Daria Zabój, product marketer at ChatBot, an AI software developer, says chatbots will be able to analyze investment questions, such as whether to invest in gold or bitcoin, in real time.
In Season 4 of the show Silicon Valley, Jian-Yang creates an app called SeeFood that uses an AI algorithm to identify any food it sees--but since the algorithm has only been trained on images of hot dogs, every food winds up being labeled "hot dog" or "not hot dog." While Jian-Yang's creation may seem absurd, in fact his app displays an intelligence that most AI models in use today do not: it only gives an answer that it knows is 100% accurate. In real life, when you ask most machine learning algorithms a question, they are programmed to give you an answer, even when they are somewhat or entirely unqualified to do so. The data on which these models are trained may have nothing to do with the specific question being asked, but the model delivers an answer anyway -- and as a result, that answer is often wrong. It's as if SeeFood tried to identify every food based only on a knowledge of hot dogs. This issue, known as "model overconfidence," is a key reason why many AI deployments fail to meet their business objectives.
Blockchain & AI are the major architecture techs of our time. Its convergence is a key factor for the present & future of tech. These emerging & foundation technologies deal with data, value storage creation and lead the digital transformation of the 4IR. The history of Artificial Intelligence AI began in antiquity, with the power of imagination – myths, stories, rumours making artificial beings endowed with intelligence or consciousness by master craftsmen, magic. The History of Blockchain & Ledgers start when the first recorded ledgers systems were found in Mesopotamia, today's Iraq, 7000 years ago.