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) …
Blockchain & AI are the major architecture techs of our time. Its convergence is a key factor for the present & future of tech. These emerging & foundation technologies deal with data, value storage creation and lead the digital transformation of the 4IR. The history of Artificial Intelligence AI began in antiquity, with the power of imagination – myths, stories, rumours making artificial beings endowed with intelligence or consciousness by master craftsmen, magic. The History of Blockchain & Ledgers start when the first recorded ledgers systems were found in Mesopotamia, today's Iraq, 7000 years ago.
I have spent most of my professional life in the age of AI and ML. During earlier times at Uber, I worked with models that estimated ETAs, calculated dynamic pricing and even matched riders with drivers. My co-founder Jason previously led video ad company TubeMogul (acquired by Adobe), which relied on ML to ensure that its advertisers didn't waste their media spend on ads that nobody saw, or ads that only bots saw. Although ride-sharing and video advertising aren't often used in the same sentence, both Jason and I faced similar challenges in ensuring that the models our companies deployed worked effectively and without bias. When models don't work as planned and machines, trained by data, make bad decisions, there is a direct impact on business results.
As an increasing number of organizations drive AI-powered digital transformation, several key trends in operationalizing AI are emerging. Growth leaders are separating themselves from growth laggards by using AI and machine learning (ML) in modern application development. Below are some statistics provided by 451 Research: Leaders invest in models for digital transformation: More than half the digital transformation leaders adopted ML compared to less than 25 percent of laggards. Furthermore, 62 percent of enterprises are developing their own models. Prevalence of DevOps increases the demand for automation: 94 percent of enterprise companies have now adopted DevOps. Models are becoming integral to the development of enterprise apps—requiring continuous, synchronized and automated development and deployment lifecycles. Data science and DevOps/app teams collaborate more: In 33 percent of enterprises, the data science/data analytics team is the primary DevOps stakeholder. An increasing number of application developers are becoming interested in data science and AI, and many have already learned the fundamentals…
Carlos M. Meléndez is the COO and Co-Founder of Wovenware, an artificial intelligence and software development company. In the early days of Covid-19, companies were preoccupied with enabling remote work, maintaining the digital infrastructure to support taxed supply chains and keeping business systems humming along. Yet, as they begin to stabilize new ways of working, they're shifting their focus back to more strategic, long-term digital transformation initiatives that leverage changing markets and new opportunities. While the pandemic caused companies to tighten their belts in 2020, in 2021, that trend is reversing, with the understanding that technology is the way forward. In fact, according to Gartner, worldwide IT spending in 2021 is expected to grow by 4% in 2021, totaling $3.8 trillion.
Artificial Intelligence (AI) technologies are being increasingly used in the Oil and Gas (O&G) industry to optimize production, reduce operational costs and maximize efficiency. According to a Markets and Markets report, AI in the global oil and gas market is expected to grow from an estimated USD 1.57 billion in 2017 to USD 2.85 billion by 2022, at a CAGR of 12.66%. The oil and gas enterprises are seeking novel approaches to address the issues that plague the industry at present. In view of the falling fuel prices, concerns over the environmental impact of energy production and personnel safety, companies are leveraging technological innovations such as AI to optimize processes and maximize the returns on investment. In this report, we present insights and trends related to the AI technologies used in the Oil and Gas industry, through a study of patents related to petroleum exploration and refining technology segments.
Medical imaging is amongst the most promising clinical applications of AI, and its ability to detect and qualify a wide arrange of medical conditions. Medical imaging is fundamental in clinical diagnosis, patient treatment and medical research. Leveraging computer-aided diagnostics can drastically improve accuracy and specificity for the detection of even the smallest radiographic abnormalities. Medical imaging produces huge datasets, which would traditionally be analysed in real-time by radiologists. However, in the light of a global pandemic, demand is mounting, and backlogs are growing.
Industry 4.0 has become more than a buzzword in the world of manufacturing; it's the new reality. A reality that has been accelerated by the Covid-19 pandemic. During the first three months of the pandemic taking hold, digital advanced the equivalent of ten years, as both businesses and consumers adjusted to an online world. The key tenets of a future-ready business - agility, resilience and innovation - can all be helped by investment in digital solutions. With the pressure on to streamline operations, reduce costs and maximise revenue, digital transformation has become an imperative.
Before the COVID-19 pandemic, business leaders weren't racing to understand the role artificial intelligence (AI) could play in optimizing business operations, boosting profitability, and driving innovation. In a 2019 survey of global executives, McKinsey found that only 58% said their companies had incorporated AI into at least one process or product. Many didn't fully grasp the potential applications and value of AI, and, as a result, adoption wasn't scaling quickly. In the same McKinsey survey, about three-fourths of companies that had adopted or planned to adopt AI said they would increase their investments over three years, the majority by just 10%. Now, since the pandemic struck, investment in AI platforms has skyrocketed, shifting from a "nice-to-have" initiative to a full-blown business imperative.
To drive business agility and accelerate IT transformation, an integrated hybrid multicloud and AI strategy is key. For a competitive advantage IT transformation has become imperative for all modern enterprises. The agility of any organisation will depend on the IT infrastructure. IT infrastructure will also define how quickly an organisation can scale and how well it can serve it's customer based on every changing dynamic digital landscape. As economies are becoming more digital – cobbled with rapidly expanding, ever evolving markets, IT infrastructure should ensure organisations must be able to match its pace.
The Covid-19 pandemic has accelerated the growth of digital economy and opened up avenues to various new digital businesses. As businesses pivot online, the need to lay a solid foundation to aid this new wave of digitalisation is vital to ensure that Malaysia can holistically transform itself towards an advanced digital economy. In 2020, a total of 45 MSC status projects were approved with investments worth RM3.9bil. This will pave the way for 3,794 new employment opportunities for Malaysians. Out of the total, RM2.6bil (66.2%) came from domestic investments, while RM1.3bil (33.8%) came from foreign investments.