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) …
Today, the term artificial intelligence (AI) is thrown around rather generously. As businesses around the world become more open to making waves and ditching legacy technologies in their quest to become data-driven, an ever-increasing number of tech deployments are claiming to use AI or machine learning (ML). But, frankly, it's often not true AI that is being used. The problem is, AI doesn't have a widely recognised definition, so it's hard to draw a line between what is AI and what isn't. In recent years, multiple businesses have invested in tools and technologies to help them understand their data, ultimately looking to maximise efficiency and provide the best possible experience for their customers.
Many mathematical algorithms that we use in data science and machine learning require numeric data. And many algorithms tend to be very complex to implement (such as Support Vector Machines or Local Linear Embedding, which we previously discussed). But, association rule mining is perfect for categorical (non-numeric) data and it involves nothing more than simple counting! What we have here is a simple algorithm with not so simplistic results! The ratio of actionable insights discovery potential (high) to algorithm complexity (low) is quite large and atypical, IMHO.
I can trace it back to when I watched a video of America's Got Talent. It started with singers, but soon it moved on to other categories, including illusionists. That was enough to tell Facebook's algorithms that I had to be interested in magic and that it should show me more of what it deduced I wanted to see. Now I have to be careful, because if I click on any of that content, it will reinforce the algorithm's notion that I must really be interested in card tricks, and pretty soon that's all Facebook will ever show me. Even if it was all just a passing curiosity.
Artificial Intelligence is no new concept. The phrase was first coined by John McCarthy in 1956, when he invited a group of researchers to discuss the notion of'thinking machines' during a conference at Dartmouth College. Since then, it has been a point of fascination for scientists, academics, software developers, and moviemakers alike. Fast-forward to today where you'll find lots of examples hiding in plain sight. From digital assistants like Amazon's Alexa or Apple's Siri, who use AI to learn from user interactions, to automated email responses and search engines predicting what you're looking for.
This series defines that environment & provides a framework to align current efforts with a 2.0 Future. What are the 2.0 Underwriting Requirements? How are new data sources, machine learning and AI, and RPA automation being used to address them? How does that change digital transformation efforts. One of InsurTech's top influencers, author, speaker and consultant in connected insurance, innovation, transformation and leadership.
Walmart has tapped Argo AI and Ford to launch an autonomous vehicle delivery service in Austin, Miami and Washington, D.C., the companies said Wednesday. The service will allow customers to place online orders for groceries and other items using Walmart's ordering platform. Argo's cloud-based infrastructure will be integrated with Walmart's online platform, routing the orders and scheduling package deliveries to customers' homes. Initially, the commercial service will be limited to specific geographic areas in each city and will expand over time. The companies will begin testing later this year.
A leading autonomous pizza machine developer is teaming up with an international pizza brand run by world-renowned chef Anthony Carron. The pandemic has been a boon for autonomous dining as takeout culture and convenience remain priorities. Restaurants have struggled to adapt to the labor demands and unpredictability of the new paradigm. Delivery options open up new opportunities to meet customers where they are, but maintaining quality is paramount. At 800 Degrees the team believed they needed to do more to future-proof the brand, and Chef Carron saw the promise of automation when a trusted industry colleague, Massimo Noja De Marco, reached out to discuss Piestro, his automated pizza venture.
Edge AI chip startup Deep Vision has raised $35 million in a series B round of funding led by Tiger Global, joined by existing investors Exfinity Venture Partners, Silicon Motion and Western Digital. The company began shipping its first-generation chip last year. ARA-1 is designed for power-efficient, low-latency edge AI processing in applications like smart retail, smart city and robotics. While the company's name suggests a focus on convolutional neural networks, ARA-1 can also accelerate natural language processing with support for complex networks such as long short-term memory (LSTMs) and recurrent neural networks (RNNs). A second-generation chip, ARA-2 with additional features for accelerating LSTMs and RNNs will launch next year.
MLOps is the machine learning operations counterpart to DevOps and DataOps. But, across the industry, definitions for MLOps can vary. Some see MLOps as focusing on ML experiment management. Others see the crux of MLOps as setting up CI/CD (continuous integration/continuous delivery) pipelines for models and data the same way DevOps does for code. Other vendors and customers believe MLOps should be focused on so-called feature engineering -- the specialized transformation process for the data used to train ML models.
Take a look at how AI companies are implementing AI. By automating procedures and operations that formerly required human intervention, Artificial Intelligence (AI) is increasing company efficiency and production. AI is also capable of comprehending data at a level that no human has ever achieved. This skill has the potential to be extremely useful in the workplace. AI has the potential to enhance every function, business, and industry.