Conversational AI is a type of artificial intelligence that facilitates the human like conversation between a human and a software system in real time. It is a piece of software that a person can talk to, like chatbot, social messaging app, interactive agent, or smart device. These applications enable users to ask questions, get opinions, find support, or complete tasks remotely. Conversational systems are powered by a conversational engine named NLP (Natural Language Processing, a branch of AI that deals with linguistic and conversational cognitive science). They make use of large volumes of data processed with machine learning, and natural language processing to aid imitate human interactions, recognizing speech and text inputs and translating their meanings in different languages. Businesses can setup automated chatbots or virtual assistants that can communicate with humans via voice or text and in different languages of user preferences.
Across midwestern farms, if Girish Chowdhary has his way, farmers will someday release beagle-sized robots into their fields like a pack of hounds flushing pheasant. The robots, he says, will scurry in the cool shade beneath a wide diversity of plants, pulling weeds, planting cover crops, diagnosing plant infections, and gathering data to help farmers optimize their farms. Chowdhary, a researcher at the University of Illinois, works surrounded by corn, one of the most productive monocultures in the world. In the United States, the corn industry was valued at $82.6 billion in 2021, but it -- like almost every other segment of the agricultural economy -- faces daunting problems, including changing weather patterns, environmental degradation, severe labor shortages, and the rising cost of key supplies, or inputs: herbicides, pesticides, and seed. Agribusiness as a whole is betting that the world has reached the tipping point where desperate need caused by a growing population, the economic realities of conventional farming, and advancing technology converge to require something called precision agriculture, which aims to minimize inputs and the costs and environmental problems that go with them. No segment of agriculture is without its passionate advocates of robotics and artificial intelligence as solutions to, basically, all the problems facing farmers today.
C3.ai (NYSE:AI) is a leading software company, which provides Artificial Intelligence services to enterprises. The company is poised to ride the wave of growth forecasted for AI. The global Artificial Intelligence (AI) market is forecasted to grow at a meteoric 20.1% CAGR from $387 billion in 2022 to over $1.3 trillion by 2029. C3.ai serves an envious list of large reputable customers from The US Air Force and the Department of Defence, to large energy companies such as Shell & Engie. They have been growing revenues at a 40% CAGR over the past couple of years, while the stock price has declined massively.
In the past five years, interest in applying artificial intelligence (AI) approaches in drug research and development (R&D) has surged. Driven by the expectation of accelerated timelines, reduced costs and the potential to reveal hidden insights from vast datasets, more than 150 companies with a focus on AI have raised funding in this period, based on an analysis of the field by Back Bay Life Science Advisors (Figure 1a). And the number of financings and average amount raised soared in 2021. At the forefront of this field are companies harnessing AI approaches such as machine learning (ML) in small-molecule drug discovery, which account for the majority of financings backed by venture capital (VC) in recent years (Figure 1b), as well as some initial public offerings (IPOs) for pioneers in the area (Table 1). Such companies have also attracted large pharma companies to establish multiple high-value partnerships (Table 2), and the first AI-based small-molecule drug candidates are now in clinical trials (Nat.
The "black-box" conundrum is one of the biggest roadblocks preventing banks from executing their artificial intelligence (AI) strategies. It's easy to see why: Picture a large bank known for its technology prowess designing a new neural network model that predicts creditworthiness among the underserved community more accurately than any other algorithm in the marketplace. This model processes dozens of variables as inputs, including never-before-used alternative data. The developers are thrilled, senior management is happy that they can expand their services to the underserved market, and business executives believe they now have a competitive differentiator. But there is one pesky problem: The developers who built the model cannot explain how it arrives at the credit outcomes, let alone identify which factors had the biggest influence on them.
Greg Nichols covers robotics, AI, and AR/VR for ZDNet. A full-time journalist and author, he writes about tech, travel, crime, and the economy for global media outlets and reports from across the U. A company that uses construction workers as roving cameramen to analyze progress on the job site has secured $60 million in Series C funding. Buildots, whose growth is tracking a broader technological turn in the practically neolithic construction sector, will use the cash to expand its product offering in a bid to be the management suite of choice for construction oversight. Construction accounts for 13% of the world's GDP, but while other traditional industries, like manufacturing, have increased productivity over the years, productivity has remained almost stagnant in the building sector.
Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. A cryptocurrency is a digital currency that may be traded without the involvement of a government or bank. On the other hand, cryptocurrencies are generated using cryptographic processes that allow users to purchase, sell, and exchange them safely.
Blockchain is the new talk of the town. It is the technology behind cryptocurrencies like Bitcoin. Today, it has turned out to be a game-changer for businesses. Its decentralized ledger offers transparency and immutability in transactions between parties without any intermediary. The transactions are irreversible, which means once a ledger is updated, it can never be changed or deleted. Blockchain technology will eventually find its space in the new and innovative applications of Machine Learning and Artificial Intelligence.
Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. It's free, we don't spam, and we never share your email address.
In the late nineteen-forties, Delmar Harder, a vice-president at Ford, popularized the term "automation"--a "nickname," he said, for the increased mechanization at the company's Detroit factory. Harder was mostly talking about the automatic transfer of car parts between machines, but the concept soon grew legs--and sometimes a robotic arm--to encompass a range of practices and possibilities. From the immediate postwar years to the late nineteen-sixties, America underwent what we might call an automation boom, not only in the automotive sector but in most heavy-manufacturing industries. As new technology made factory work more efficient, it also rendered factory workers redundant, often displacing them into a growing service sector. Automation looks a little different these days, but the rhetoric around it remains basically the same.