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) …
To most of us, a 3-D-printed turtle just looks like a turtle; four legs, patterned skin, and a shell. But if you show it to a particular computer in a certain way, that object's not a turtle -- it's a gun. Objects or images that can fool artificial intelligence like this are called adversarial examples. Jessy Lin, a senior double-majoring in computer science and electrical engineering and in philosophy, believes that they're a serious problem, with the potential to trip up AI systems involved in driverless cars, facial recognition, or other applications. She and several other MIT students have formed a research group called LabSix, which creates examples of these AI adversaries in real-world settings -- such as the turtle identified as a rifle -- to show that they are legitimate concerns.
Preparing for and implementing AI projects can be a multi-year journey. According to the latest figures, only 28% of respondents reported getting past the AI planning stage in the first year. This is due to several factors including the relative maturity of the technology (at least in the ever-expanding set of industry use cases), the level of complexity involved such as extensive integration requirements, limited enterprise experience and lack of internal skill sets, concerns with AI bias as well as governance, risk and compliance concerns, extensive change management requirements and more. With so much emphasis on demonstrating quick wins, whether as part of corporate innovation programs or digital transformation initiatives, over-long AI projects can potentially impact the reputations of much larger initiatives than just their own. As CIOs move from "projects to products" in their approach to product management, these lengthy AI projects can delay innovative new internal or external product releases as well.
Have you ever thought your product's progress was headed in one direction, and been shocked to see a different story reflected in big picture KPIs like revenue? It can be confusing when customer feedback or metrics like registration or retention are painting a different picture. No matter how sophisticated your analytics are, if you're asking the wrong questions - or looking at the wrong metrics - you're going to have trouble getting answers that can help you. Join Nima Gardideh, CTO of Pearmill, as he demonstrates how to build a strong data culture within your team, so everyone understands which metrics they should actually focus on - and why. Then, he'll explain how you can use your analytics to regularly review progress and successes.
ABOUT: You want to include a machine learning component in your IT systems? The process is a little more involved than clicking through an AI tutorial on your laptop. It's not just the first working model you run that you need to consider; you also need to think about things like integration, scaling, and testing. What's more, postlaunch, you'll want to continuously adapt your model to respond to the changing environment. Christoph and Arif will give an introduction into Continuous Delivery for Machine Learning (CD4ML) - a set of tools and processes that ensure that software under development in Machine Learning can be reliably released to production at any time and with high frequency.
Another day, another AI fail. Dismal project success rates between 10% to 50% continue to plague countless artificial intelligence programs. Surveys from Gartner, IDC, O'Reilly and other sources reveal most organizations run into the same preventable challenges when embarking on an AI journey. What can you do to improve your chance for AI success? In this article, I'll share several tips that I've learned from working with exceptional AI-driven enterprises around the world.
Manatee, a Denver startup specializing in AI apps for people with autism, is working with a company called Robauto to developing a robot called BiBli that can talk children through challenging interactions without judgment--at the child's own pace. Manatee co-founder and CEO Damayanti Dipayana recognizes both the benefits and limitations of a technology like BiBli: "I don't think AI can provide all kinds of therapy, but it's a scalable way to provide care for kids who wouldn't get care," she tells Verywell. Many kids with autism or anxiety disorder find it easier to talk with the screen or the robot. In the long run, the information collected by a robot or app can be analyzed and shared with a therapist to provide a therapist with insight into what issues are challenging."
Voice and the rise of home devices and smart speakers are opening up new possibilities for researchers, enabling respondents to engage beyond simply typing a response and creating opportunities for ongoing dialogue. In an ESOMAR paper, What market research can learn from Alexa & Siri, a trio of authors – Young Ham (Kantar Australia), Jason Dodge (Kantar US) and Rebecca Southern (Kantar Australia) – extol the benefits of chatbots and AI. "These can help bridge the gap between quantitative and qualitative, offering more in-depth ways to better understand today's consumers," they write. "These give the chance to participate in a more interactive, flowing manner that is more conversational than a typed response." And for marketing and insights teams, they add, "AI can deliver smarter, more impactful consumer engagement... at scale".
Businesses are using voice assistants to answer rote, repetitive questions faster and cheaper so HR, IT and other departments can focus on higher value tasks. In some cases, businesses are just replacing website and internal portal Frequently Asked Questions (FAQs) pages with simple, deterministically programmed chatbots. In other cases, they're replacing or supplementing what employees have traditionally done with digital counterparts that use machine learning. "Before one of our clients decided to implement a recruitment voice assistant, HR department employees had to personally process all phone calls from job seekers. They also had to create a new profile for every new candidate and fill in the information manually, which took a lot of time," said Julia Ryzh, chief marketing officer at voice experience platform provider Just AI. "It was [also] hard to tell whether the candidate had already applied for the position in question, since all the necessary pieces of information were stored in different places."
Businesses across the world are hiring data scientists to beef up their efficiency and competitiveness via artificial intelligence (AI). Startup companies (dubbed AI-First companies) are disrupting traditional industries like banking, insurance, real estate and healthcare using AI technologies. The demand for data scientists far exceeds supply. And, the problem is exacerbated by the fact that the data scientist profession is itself splitting into multiple sub-disciplines. Regardless of which of these skill-sets are needed, businesses face a common problem when trying to monetize successful AI experiments.