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) …
Browse 64 Market Data Tables and 37 Figures spread through 133 Pages and in-depth TOC on "Humanoid Robot Market - Global Forecast to 2023" The humanoid robot market for software is expected to grow at a higher CAGR during the forecast period. As the technological advancement will lead to the growing complexity in terms of features such as AI and autonomous operations, the value of the software part in the robot will grow faster than hardware as software will assist the complex functionalities to process efficiently and accurately. The biped motion type captured a larger share of the overall humanoid robot market in 2016. The actual human-like appearance can be realized in humanoids only when the robot is capable of walking on feet like humans; owing to this, a majority of the humanoid robot manufacturers are focusing on their designs to make biped robots. The Americas accounted for the largest share of the overall humanoid robot market in 2016.
As robotics technologies have advanced significantly in the past few years, robots for enterprise markets are becoming more affordable, productive, and smarter than ever before. According to a new report from Tractica, this trend is resulting in a significant increase in the number of enterprises within the agriculture, construction, warehousing and logistics, telepresence, customer service, and other sectors willing to invest in robots to cut costs and increase profits, while trusting robots to solve business challenges and productivity gaps. Tractica forecasts that worldwide shipments of enterprise robots will grow from approximately 83,000 units in 2016 to 1.2 million units annually by 2022, increasing at a compound annual growth rate (CAGR) of 57% during that period. Global revenue for the enterprise robotics market will increase from $5.9 billion in 2016 to $67.9 billion in 2022. "Just as robotics has transformed manufacturing and heavy industry in recent years, robots are beginning to impact enterprise work processes, with innovative and effective solutions being introduced with increased frequency," says research analyst Manoj Sahi.
According to the "Artificial Intelligence in Marketing Market by Offering (Hardware, Software, Services), Technology (Machine Learning, Context-Aware Computing, NLP, Computer Vision), Deployment Type, Application, End-User Industry, and Geography - Global Forecast to 2025", published by MarketsandMarkets, the market is expected to be valued at USD 6.46 Billion in 2018 and is likely to reach USD 40.09 Growth in the adoption of customer-centric marketing strategies, increase in demand for virtual assistants, and increased use of social media for advertising are the major factors driving the demand for AI-based marketing and sales solutions. Browse 67 tables and 59 figures spread through 200 pages and in-depth TOC on "Artificial Intelligence in Marketing Market - Global Forecast to 2025" Software holds a major share of the overall AI in marketing market owing to the developments in AI software and related software development kits. AI systems require different types of software, including application program interfaces, such as language, speech, vision, and sensor data, along with machine learning algorithms, to realize various applications for sales and marketing. Software platforms and solutions are available at high costs as there are limited number of experts that develop machine learning algorithms.
Artificial intelligence (AI) processing today is mostly done in a cloud-based data center. The majority of AI processing is dominated by training of deep learning models, which requires heavy compute capacity. In the last 6 years, the industry has experienced a 300,000X growth in compute requirements, with graphics processing units (GPUs) providing most of that horsepower. According to a new report from Tractica, however, as the diversity of AI applications grows, an increasing amount of AI processing will be handled within edge devices rather than in a centralized, cloud-based environment. Tractica forecasts that AI edge device shipments will increase from 161.4 million units in 2018 to 2.6 billion units worldwide annually by 2025.
Significant advances have been made during the past few years in the ability of artificial intelligence (AI) systems to recognize and analyze human emotion and sentiment, owing in large part to accelerated access to data (primarily social media feeds and digital video), cheaper compute power, and evolving deep learning capabilities combined with natural language processing (NLP) and computer vision. According to a new report from Tractica, these trends are beginning to drive growth in the market for sentiment and emotion analysis software. Tractica forecasts that worldwide revenue from sentiment and emotion analysis software will increase from $123 million in 2017 to $3.8 billion by 2025. The market intelligence firm anticipates that this growth will be driven by several key industries including retail, advertising, business services, healthcare, and gaming. According to Tractica's analysis, the top use case categories for sentiment and emotion analysis will be as follows: "A better understanding of human emotion will help AI technology create more empathetic customer and healthcare experiences, drive our cars, enhance teaching methods, and figure out ways to build better products that meet our needs," says principal analyst Mark Beccue.
Imagine this scenario: An executive's primary competitor has just hired a promising start-up, a software firm specializing in strong artificial intelligence that covers a broad range of applications, not just something specific such as translation or image recognition. Trying to learn more about this, she looks up the start-up founder's TEDx talk but finds it impenetrable. The firm's website boasts claims for the tool bordering on science fiction and has pictures of young men and women and their dogs in a WeWork space against a backdrop of whiteboards all covered in formulas. How solid is this technology? How does what the people with the dogs say they do intersect with what their technology seems to be for?
This article was written by Kwon Sok Oh, a Financial Analyst at I Know First. NVIDIA Corp. (NASDAQ: NVDA) is the leading designer and manufacturer of graphics processing units and related products and services. It's main operating segments are the GPU segments and the Tegra Processor segment. NVIDIA currently has 11,528 employees with $9.71 billion in revenue in 2017. It is headquartered in Santa Carla, CA.
According to the new research report "Artificial Intelligence in Supply Chain Market by Offering, Technology, Application (Fleet Management, Supply Chain Planning, Warehouse Management, Virtual Assistant, Freight Brokerage), End-User Industry, and Geography - Global Forecast to 2025", published by MarketsandMarkets, the market is expected to grow from USD 730.6 million in 2018 to USD 10,110.2 million by 2025, at a CAGR of 45.55% between 2018 and 2025. Major drivers for the market are the growth of big data, demand for greater visibility and transparency into supply chain data and processes, and adoption of AI for improving consumer services and their satisfaction. The major restraint for the market is the limited number of the artificial intelligent technology experts. Browse 64 market data Tables and 44 Figures spread through 176 Pages and in-depth TOC on "Artificial Intelligence in Supply Chain Market - Global Forecast to 2025" The software market is expected to grow at the highest CAGR for artificial intelligence in supply chain by 2025. The adoption of AI-based software solutions is increasing as it has beginning to apply machine learning capabilities that can automatically detect errors and make course corrections while processing real-time data streams.