When Deloitte's recent State of AI in the Enterprise study asked AI adopters about their organization's top adoption challenges, "managing AI-related risks" topped the list--tied with integration and data challenges, and on par with implementation concerns.1 And while worry is high, action to ameliorate risks is lagging: Fewer than one-third practice more than three AI risk management activities.2 And fewer than four in 10 adopters report that their organization is "fully prepared" for the range of AI risks that concern them. To investigate whether actively managing AI risks has any tangible benefit, we compared two groups of AI adopters that approach those risks differently: Risk Management Leaders (11%) undertake more than three AI risk management practices and align their AI risk management with their organization's broader risk management efforts, while Risk Management Dabblers (51%) undertake up to three AI risk management practices but are not aligning them with broader risk management efforts.3 The Leaders believe AI has greater strategic importance to their business: 40% see AI as "critically important" to their business today, versus only 18% of the Dabblers--and within two years, those numbers are expected to rise to 63% and 36%, respectively.
Hosted by Dylan Doyle-Burke and Jessie J Smith, Radical AI is a podcast featuring the voices of the future in the field of artificial intelligence ethics. In this episode Jess and Dylan chat to Ryan Calo about robot regulation. What is robot regulation and why does it matter? To answer this question we welcome to the show Ryan Calo. Ryan is a professor at the University of Washington School of Law.
The application of emerging technologies such as AI, cloud, blockchain and IoT in financial services has altered the traditional operating models of financial institutions, the competitive dynamics of the industry, the role of people in those institutions and the landscape of the financial system as a whole. In fact, AI is positioned as an essential investment, with the World Economic Forum arguing how it is set to become central to the fabric of financial institutions. While the adoption of AI in financial services may be in its infancy, the use cases are ever growing. From recommending loan and credit offerings to detecting fraud, 94% of financial services in European and Middle Eastern markets believe that AI will disrupt their business. The direction and the awareness of AI is clear but it is essential that companies invest now, as if done too hastily, the process is marred by pitfalls.
In recent years, technology is increasingly being used in a range of ways to make construction more efficient and innovative. It is no longer odd to fly a drone over a construction site, to optimise work schedules to improve workplace safety or choose the best setting based on predictions. Despite a retarded initial adoption pace, construction leaders are beginning to take a greater interest in the transformative prospects of AI tech. During the next upcoming years, expect an increasingly quick rate for tech acceptance as applications and products targeted for construction continue hitting the market. Most megaprojects go over budget despite employing the best project teams.
Artificial intelligence is growing at a rapid pace to the point where it is making important decisions for us. While this can be beneficial in some ways, AI algorithms which discriminate or have a bias in the decision-making process can result in unprecedented repercussions for individuals or sections of society. Algorithms are, in the end, developed by human beings, and humans come with biases which can reflect in algorithms. This has happened in the past. As the tech enterprises developing these algorithms come under fire, many are taking initiatives to address the issue.
Speaking of the Millennial and the next generation, which distinguishes us from our predecessors is discovery, humans have now created and further built almost everything we can touch virtually. The only thing that is common among us, our predecessors and the next generation is the brain – which changes our communication behavior and how we view things. Artificial intelligence, most commonly known as AI has been a forecast for decades but was initially associated with robots only. However, at that time, AI joined in almost everything we used and called smart. AI is something where software acts as a human; show behavior like humans.
We are seeing overwhelming growth in AI/ML systems to process oceans of data that are being generated in the new digital economy. However, with this growth, there is a need to seriously consider the ethical and legal implications of AI. As we entrust increasingly more sophisticated and important tasks to AI systems, such as automatic loan approval, for example, we must be absolutely certain that these systems are responsible and trustworthy. Reducing bias in AI has become a massive area of focus for many researchers and has huge ethical implications, as does the amount of autonomy that we give these systems. The concept of Responsible AI is an important framework that can help build trust in your AI deployments.
A pair of researchers from the Oak Ridge Laboratory have developed an "explainable" AI system designed to aid medical professionals in the diagnosis and treatment of children and adults who've experienced childhood adversity. While this is a decidedly narrow use-case, the nuts and bolts behind this AI have particularly interesting implications for the machine learning field as a whole. Plus, it represents the first real data-driven solution to the outstanding problem of empowering general medical practitioners with expert-level domain diagnostic skills – an impressive feat in itself. Let's start with some background. Adverse childhood experiences (ACEs) are a well-studied form of medically relevant environmental factors whose effect on people, especially those in minority communities, throughout the entirety of their lives has been thoroughly researched. While the symptoms and outcomes are often difficult to diagnose and predict, the most common interventions are usually easy to employ.
Artificial intelligence (AI) is steadily becoming a familiar tool for many Australians. We have come to know it through our pocket voice assistants, like Siri and Alexa, and as the brains behind Google's predictive searches. Australian businesses, particularly in the mining sector, view it as a means to gain a competitive advantage, and we have even seen its potential to fight COVID-19. As AI begins to permeate every aspect of our lives, the Australian government has recognised the economic and social opportunities it affords us in its newly proposed AI Action Plan. The discussion paper, released on 29 October 2020, is the latest in a suite of Australian initiatives targeting AI regulation and development, following on from the AI Ethics Framework.
The American workforce is at a crossroads. Digitization and automation have replaced millions of middle-class jobs, while wages have stagnated for many who remain employed. A lot of labor has become insecure, low-income freelance work. Yet there is reason for optimism on behalf of workers, as scholars and business leaders outlined in an MIT conference on Wednesday. Automation and artificial intelligence do not just replace jobs; they also create them.