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) …
The partnership will see IonQ and Accenture join forces to help other businesses assess how quantum computing could help improve their outcomes. Quantum computing start-up IonQ has signed off a new collaboration with consulting giant Accenture, in a move that shows once more that the technology is coming further out of the lab and into the business strategies of forward-looking executives. The partnership will see IonQ and Accenture join forces to help other businesses assess how quantum computing could help improve their outcomes. With Accenture's client-list spanning 120 countries and more than three-quarters of the Fortune Global 500, this could go a long way toward bringing quantum computing further into the mainstream. Accenture works with C-suite executives to assist them with their digital transformation goals.
We all know about the paradigm-changing use of AI for Netflix recommendations, chatbots that impersonate customer service agents online, and the dynamic pricing of hotel rooms. Such efforts are the value creation engines of countless large, successful companies. But organisations can also adopt a decidedly less splashy and, at face value, more pedestrian use of AI--to process documents faster and simplify operational procedures. Although this use is aimed at reducing costs rather than transforming industries, 'boring AI' is actually quite exciting--because it confronts issues that all companies wrestle with, and because the gains in productivity are real. Recent research by PwC on automating analytics found that even the most rudimentary AI-based extraction techniques can save businesses 30–40% of the hours typically spent on such processes.
The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Deloitte Consulting published a report today that suggests a golden age of AI is in the offing, assuming organizations can implement and maintain a consistent approach to machine learning operations (MLOps). Citing market research conducted by AI-focused Cognilytica, the MLOps: Industrialized AI report from Deloitte notes that the market for MLOps platforms is forecast to generate annual revenues in excess of $4 billion by 2025. Several startups are already focused on providing these platforms. Less clear, however, is the degree to which MLOps might become an extension of the DevOps platforms many organizations rely on today to build and deploy software.
When working with their clients Accenture under Tricarico's guidance focuses on "on guiding (their) clients to more safely scale their use of AI, and build a culture of confidence within their organizations." Not all companies have an established north star of AI use. Companies and partners like Accenture are vital to these companies and their proper and ethical use of the technology.
Conversational AI solutions--including chatbots, virtual agents, and voice assistants--have become extraordinarily popular over the last few years, especially in the previous year, with accelerated adoption due to COVID-19. Data from various conversational AI vendors showed that the volume of interactions handled by conversational agents increased by as much as 250% in multiple industries.6 These solutions are already delivering significant value for many organizations. Around 90% of companies mentioned faster complaint resolution and over 80% reported increased call volume processing using conversational AI solutions, according to a recent survey.7 However, the technology still suffers from a number of limitations that make it difficult to use and limit its value.
This article was originally published on our sister site, Freethink. A financial consulting firm has created AI avatars for its staff, which they can use to quickly create deepfakes of themselves for presentations, emails, and more. The challenge: During the pandemic, remote work became the norm at many companies, and meetings that might have once taken place over lunch happened over the internet instead. This transition was more difficult for some industries than others, and those that traditionally relied on face-time with clients to build relationships and secure deals may have struggled to find their footing. "[W]hile much has been written about how to collaborate remotely with coworkers … companies still are trying to figure out the best way to connect with clients over teleconferencing platforms," Snjezana Cvoro-Begovic and James Hartling, execs at the software company Cognizant Softvision, wrote in Fast Company.
After decades as science fiction fantasy, artificial intelligence (AI) has made the leap to practical reality and is quickly becoming a competitive necessity. Yet, amidst the current frenzy of AI advancement and adoption, many leaders and decisionmakers still have significant questions about what AI can actually do for their businesses. This dossier highlights dozens of the most compelling, business-ready use cases for AI across six major industries. Each use case features a summary of the key business issues and opportunities, how AI can help, and the benefits that are likely to be achieved. The dossier also includes several emerging AI use cases for each industry that are expected to have a major impact in the future.
Emerging technologies are reshaping businesses and have emerged as a key disruptor of our times. Digital is providing breakthrough capabilities at all levels of value chain and there is no ambiguity that we are living in the age of digital disruption with digital technologies not only redefining the business models but also how organisations operate. With the advancement in connectivity and cloud / edge computing, new use cases enabled by AI / ML, IoT, Robotics and AR / VR have found widespread mainstream adoption. This transformation will be further accelerated with 5G adoption.
The biggest challenge implementing artificial intelligence is moving from concept to scale. A new report from Deloitte finds that in consumer-related businesses, the challenge is especially difficult because many have large legacy data and analytics platforms, and decentralized data and analytics operations. Another common obstacle is achieving alignment and integration across business units and among IT stakeholders. These consumer businesses include consumer products, retail, automotive, lodging, restaurants, travel and transportation. Yet, "consumer-related businesses are actively exploring ways to harness the power of AI, and many valuable use cases are emerging,'' according to the report, The AI Dossier. However, AI adoption and maturity levels vary widely for reasons including scalability due to data quality and complexity, organizational constructs and talent scarcity, and lack of trust, the report noted. For each industry, the report highlighted the most valuable, business-ready use cases for AI-related technologies and examined the key business issues and opportunities, how AI can help and the benefits that are likely to be achieved. The report also highlighted the top emerging AI use cases that are expected to have a major impact on the industry's future. For example, in customer service, one of the largest segments of customer relationship management, it is now possible to personalize the customer experience across all channels, using machine learning, conversational AI and natural language processing through the customer journey and lifecycle, the report said. SEE: Digital transformation: A CXO's guide (free PDF) (TechRepublic) AI can help by automating customer interactions through the use of chatbots. It can also be used in tandem with Internet of Things devices to sense the sentiments and needs of connected customers and to personalize the customer experience, the report said. Consumer demand planning, forecasting and marketing will also be enhanced through AI, the report said. "As the number of sales channels used by consumers continues to grow, retailers should continue to improve how they plan across multiple sales channels--and how they handle disruptions.
By 2025, artificial intelligence (AI) will significantly improve our daily life by handling some of today's complex tasks with great efficiency. The leading AI researcher, Geoff Hinton, stated that it is very hard to predict what advances AI will bring beyond five years, noting that exponential progress makes the uncertainty too great. This article will therefore consider both the opportunities as well as the challenges that we will face along the way across different sectors of the economy. It is not intended to be exhaustive. AI deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. Some of the classical approaches to AI include (non-exhaustive list) Search algorithms such as Breath-First, Depth-First, Iterative Deepening Search, A* algorithm, and the field of Logic including Predicate Calculus and Propositional Calculus. Local Search approaches were also developed for example Simulated Annealing, Hill Climbing (see also Greedy), Beam Search and Genetic Algorithms (see below). Machine Learning is defined as the field of AI that applies statistical methods to enable computer systems to learn from the data towards an end goal. The term was introduced by Arthur Samuel in 1959. A non-exhaustive list of examples of techniques include Linear Regression, Logistic Regression, K-Means, k-Nearest Neighbour (kNN), Naive Bayes, Support Vector Machine (SVM), Decision Trees, Random Forests, XG Boost, Light Gradient Boosting Machine (LightGBM), CatBoost. Deep Learning refers to the field of Neural Networks with several hidden layers. Such a neural network is often referred to as a deep neural network. Neural Networks are biologically inspired networks that extract abstract features from the data in a hierarchical fashion.