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) …
If the power of logical reasoning is able to optimize the resources needed to reach quality AI solutions in a nonconventional way, then the AI industry should prepare for a major upcoming change. It is a change that is built on creativity; regardless of application titles or goals, no two applications will have the same results. Companies strive to transform their ideas into working plans to achieve their tactical goals. They do have highly specialized teams to make this happen, but not many companies in the AI realm have the strategic view of what may soon emerge in the industry. Having a highly specialized crew is indeed crucial to achieve tactical objectives.
To help facilitate this, we have developed the Mindful AI approach to help businesses develop AI solutions that are more valuable because they are relevant and useful to the people they serve – rather than just the producers of the platform. We make AI technology more inclusive by working with under-represented communities through the diversity we need for our data programs (age, gender, groups, genre, geographies, ethnicities, cultures, and languages) to help our customers build more inclusive AI-based products and to reduce bias. Our expertise with AI localization makes it possible for our clients to make their AI applications more inclusive and personalized, respecting critical nuances in local language and user experiences that can make or break the credibility of an AI solution from one country to the next. By designing an AI application more mindfully from the start, a business will set itself up to be more effective and inclusive in its development of AI applications that help people no matter where they live. To help facilitate this, we have developed the Mindful AI approach to help businesses develop AI solutions that are more valuable because they are relevant and useful to the people they serve – rather than just the producers of the platform.
In a recent McKinsey Future of Work podcast interview with Microsoft Chief Technology Officer Kevin Scott, the CTO revealed that artificial intelligence (AI) is about to go prime time and will begin showing up in the most unlikely of places – including our nation's farm fields Once a mysterious science being beta-tested by only Fortune 100 companies, AI is rapidly becoming more democratic, inclusive, and utilitarian – to even those residing in under-served communities. It's also becoming a versatile tool that can branch out to myriad market sectors, including one of our oldest – agriculture. Scott recently published a book entitled Reprogramming the American Dream: From Rural America to Silicon Valley – Making AI Serve Us All. The findings come from his personal experiences with AI being implemented to service populations in rural towns and working-class communities, rather than just hi-tech cities or corner offices. The Wall Street Journal recently reported on Microsoft's FarmBeats program – a platform that leverages AI to improve farming outcomes.
The capabilities of artificial intelligence (AI) for retailers of all different shapes and sizes has undeniably grown across many sectors in recent years. In today's world, retailers are beginning to develop a legitimate recognition of what it takes to properly appraise, develop and generate AI and ML-enabled solutions of the future, moving past the marketing outbreak that AI once was. Moreover, despite the developments that have been contrived, some retailers have not yet acknowledged the true possibilities of AI and what this entails. It is these retailers that need to question themselves: what do we want to accomplish with AI? What can AI really deliver – and what will this mean for our customers? The opportunities to leverage AI and ML to improve retail operations are exponential for either online, in store or in the warehouse.
Artificial intelligence is a major factor in people’s lives. It influences who gets a loan, how companies hire and compensate employees, how customers are treated, even where infrastructure and aid are allocated. It is already deeply embedded in our businesses, organizations and governments, including the 40,000 client engagements with IBM Watson across 20 industries in 80 countries. As the world increasingly relies on AI to help make major predictions and decisions, it becomes essential that people can trust the process and results of that AI. IBM is working on building that trust. Organizations that neglect their ethical duties in AI can face lawsuits, regulatory fines, angry customers, embarrassment, reputational damage, and destruction of shareholder value. For example, consider the fairness of your organization’s hiring practices. If your HR department uses an existing machine-learning-based application to score prospective employees, how do you ensure trustworthy implementation of this technology? From a technical perspective, governed data and AI technology should…
In recent years, the vast majority of the enterprises that invested in Artificial Intelligence (AI) capabilities fell into one of two categories: those who used AI applications successfully to improve operations or cut costs and those who were participating in what Goutham Belliappa, vice president of AI engineering at Capgemini North America calls "AI theater:" They implemented AI models "to create some buzz in the marketplace, but they didn't go through the hard work of tying their AI capabilities to business value," Belliappa says. Today, companies stand on the precipice of a new era. "AI is on the cusp of a tremendous economic impact that will disrupt every industry in the same way that software was positioned about thirty years ago," says Brian Jackson, analyst and research director at Info-Tech Research Group. "AI's rapidly growing capabilities are being applied to solve problems in far more efficient ways than we were able to do previously." As a result, forward-looking IT leaders are revisiting and rethinking their AI strategies for the future.
Every industry faces operational challenges on an everyday basis, whether it is due to machine downtime or equipment failure. However, the latest advents in technology like artificial intelligence; IoT, etc. help industries to tackle such challenges efficiently. After witnessing this, the oil and gas industry has finally started the integration of these technologies in its operations. There are various applications of artificial intelligence for different industries. Out of which, the main applications of AI for the oil and gas industry are machine learning (ML) and data science.
The foundation of enterprise intelligence is technology platform that is increasingly being driven by artificial intelligence (AI). IDC has identified three pillars that drive enterprise intelligence: 1) an organization's ability to synthesize information, 2) its capacity to learn and 3) its ability to apply insights at scale. AI has immense potential to super-charge all three of these pillars. However, most enterprises still struggle with AI, and achieving enterprise intelligence at scale remains a challenge for most organizations. According to IDC's 2020 survey of analytics, AI, and RPA services buyers, 80% of respondents said they were at some stage of AI adoption, though most were only in pilots or using AI for limited business functions.
Nowadays, companies aim to increase their profits by building an artificial intelligence solution using their data. However, since they often do not have perfect data strategies in place, they do not succeed in their mission. The data strategy has four main pillars: Value, Collection, Architecture, and Governance. I described the data strategy at How to Create a Data Strategy for Your Organization. I have seen common mistakes across companies aiming to create data strategies.
Most of what goes by the name of Artificial Intelligence (AI) today is actually based on training and deploying Deep Learning (DL) models. Despite their impressive achievements in fields as diverse as image classification, language translation, complex games (such as Go and chess), speech recognition, and self-driving vehicles, DL models are inherently opaque and unable to explain their predictions, decisions, and actions. This is not a critical issue for several applications (such as movie recommendation systems or news/social media feed customization, for example) where the end user will evaluate the quality of the AI based on the results it produces, make occasional adjustments to help it improve future results (e.g., by rating additional movies), or move away from that product/app. There is rarely a need to require an explanation for the AI's decisions when there is very little at stake. However, for high-stakes situations and mission-critical applications – such as self-driving vehicles, criminal justice decisions, financial systems, and healthcare applications – explainability might be considered crucial.