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) …
As Business Insider reports, "most banks (80 percent) are highly aware of the potential benefits presented by AI and machine learning. In fact, many banks are planning to deploy solutions enabled by AI: 75 percent of respondents at banks with over $100 billion in assets say they're currently implementing AI strategies, compared with 46 percent at banks with less than $100 billion in assets," according to a UBS Evidence Lab report. Business Insider further notes that many banks are using AI in front-office applications "to smooth customer identification and authentication, mimic live employees through chatbots and voice assistants, deepen customer relationships, and provide personalized insights and recommendations." In middle-office functions, the UBS report states, AI is being used to assess risk, detect and prevent fraud, improve processes to prevent money laundering, and perform know-your-customer regulatory checks. "The winning strategies employed by banks that are undergoing an AI-enabled transformation reveal how to best capture the opportunity. These strategies highlight the need for a holistic AI strategy that extends across banks' business lines, usable data, partnerships with external partners, and qualified employees."
The financial services sector has been an eager adopter of robotic process automation (RPA): by one estimate, it accounts for 29% of the RPA market, more than any other sector. So it stands to reason that the industry is an early adopter of intelligent automation, the combination of RPA with AI. "Financial services [institutions] have always been among of the top adopters of intelligent automation," says Sarah Burnett, industry analyst and evangelist at process mining vendor KYP.ai. Financial institutions have adopted a range of use cases for intelligent automation, from simple integrations of cognitive services into RPA systems to, in a few cases, AI-powered decision making. As such, they have also encountered the security risks and governance challenges that arise from intelligent automation sooner than most. Intelligent automation is a broad term, representing a range of possibilities for integrating AI and machine learning into process automation.
In this paper we present a novel agent-based modeling methodology to predict rooftop solar adoptions in the residential energy market. We first applied several linear regression models to estimate missing variables for non-adopters, so that attributes of non-adopters and adopters could be used to train a logistic regression model. Then, we integrated the logistic regression model along with other predictive models into a multi-agent simulation platform and validated our models by comparing the forecast of aggregate adoptions in a typical zip code area with its ground truth. This result shows that the agent-based model can reliably predict future adoptions. Finally, based on the validated agent-based model, we compared the outcome of a hypothesized seeding policy with the original incentive plan, and investigated other alternative seeding policies which could lead to more adopters.
India is among the top 10 nations in the world in terms of technological advancements and funding in artificial intelligence, according to findings from a study published by The Brookings Institution. While India is outside the top 10 in terms of commercial and research initiatives in artificial intelligence (AI), it ranks sixth in terms of spending and investments on AI made by public, governmental initiatives, as well as private institutes and organisations. The study notes that alongside having increasing adoption of new generation technologies, India is "well positioned from the funding standpoint" – a factor that gives it leverage to quickly achieve faster innovations in AI technologies, and overtake other nations that are leading AI achievements right now. The other nations leading AI achievements ahead of India are USA, China, United Kingdom, France, Japan and Germany. Canada, South Korea and Italy are the other three nations behind India in the top 10 AI adopters list, as per the study.
The study notes that alongside having increasing adoption of new generation technologies, India is "well positioned from the funding standpoint" – a factor that gives it leverage to quickly achieve faster innovations in AI technologies, and overtake other nations that are leading AI achievements right now. The nations leading AI achievements ahead of India are the US, China, the UK, France, Japan and Germany. Canada, South Korea and Italy are behind India in the top 10 AI adopters list, as per the study. The study corroborates the fact that AI adoption across various sectors in India has been growing rapidly. A December 2021 report by McKinsey Analytics on the state of AI in 2021 found that India was the leading adopter of AI among emerging economies, from a commercial, business standpoint.
Dean Chester is a cybersecurity expert. Artificial Intelligence (AI) tools and resources have become indispensable to today's industry. The 2019 study by Gartner shows that in the last four years, the use of AI has increased by 270%. In the last year alone, the number of organizations that have deployed AI in some way has more than triples from 4% to 14%. Why is AI becoming so popular with businesses of all sizes?
From headline-making cyber-physical systems and cloud computing to the internet of things (IoT), cognitive computing or artificial intelligence (AI), complementary and consequential emerging technologies are changing how businesses and institutions operate. From Industry 4.0 innovations to smart cities and supply chain optimization, technological breakthroughs are shaking foundational assumptions and facilitating enormous leaps forward. The power and possibilities these new technologies unlock are based on one key unifying element: data. The ability to gather, manage, analyze and utilize vast amounts of data -- discovering new connections and gleaning new insights -- is a true game-changer across a wide range of applications. Powerful, sophisticated data analytics tools and strategies are no longer luxuries, but rather critical necessities for competitive brands and businesses in many industries.
Deloitte introduced ReadyAI, a full portfolio of capabilities and services to help organizations accelerate and scale their artificial intelligence (AI) projects. ReadyAI brings together skilled AI specialists and managed services in a flexible AI-as-a-service model designed to help clients scale AI throughout their organizations. The AI market is expected to exceed $191 billion by 2024, growing at 37% compound annual growth rate. As organizations accelerate their adoption of AI, many struggle with challenges such as limited access to specialized talent, slow development cycles, and the resources to continuously maintain AI models. Creating and sustaining AI models at scale typically requires people with capabilities across data science, IT operations and user experience (UX) who work seamlessly towards a common goal.
We've seen widespread disruption, change and uncertainty in every sphere of business. Yet, chaotic, unstable times tend to also bring great leaps forward in terms of technology and innovation. In 2020, I've seen numerous enterprises discover just how much AI and ML tools can help their organization remain stable and even continue to grow despite the turmoil rolling through the markets. But this growth comes with the necessity to assure the health of ML models in production to avoid drifts, biases and anomalies. While AI adoption has taken a giant leap forward, we've learned that ML models need to be adaptable and robust.
Deloitte has launched the Deloitte Center for AI Computing, designed to accelerate the development of artificial intelligence offerings for its clients. The center is built on NVIDIA's DGX A100 systems to create a supercomputing architecture that will help Deloitte's clients in their efforts to become AI-fueled organizations. The accelerated computing platforms feature NVIDIA graphics processing unit technology, along with its networking and software for advanced data processing, analytics and AI by bringing massive parallel processing capability and speed to deep learning, machine learning and data science workloads, the company said. Deloitte's State of AI in the Enterprise survey found that more than half of respondents reported spending more than $20 million over the past year on AI technology and talent. Nearly all adopters said they were using AI to improve efficiency, while mature adopters are also harnessing the technologies to boost differentiation.