This week, AI Reskilling: 4 Excellent Reasons This Is A Sensible Solution To The Worker Crisis.
Welcome to The Digital Eye, your weekly roundup of the latest technology news.
Our team of experts have scoured the internet for the most exciting and informative articles so that you can stay up-to-date on all things digital, data, blockchain, AI & analytics.
- 3 Strategies To Redefine Your Executive Career Path With AI
- Is diversity the key to collaboration? New AI research suggests so
- Mitigating ESG risk in AI systems through AI quality
- Could machine learning and operations research lift each other up?
- There is no better time for fintech banking solutions
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Artificial Intelligence (AI) is disrupting businesses and job roles in every industry, causing concerns about long-term job security for low-skill manual jobs and management roles alike.
To prepare for this AI-driven economy, many experienced managers and seasoned executives are turning to MOOCs (Massive Open Online Courses) to upskill in foundational data analytics and AI. This trend is unlikely to slow down anytime soon: The global MOOC market is expected to grow from $3.9 billion in 2018 to $20.8 billion by 2023, a CAGR of 40.1 percent.
Article by @MIT
Is diversity the key to collaboration? New AI research suggests so
A new training approach yields artificial intelligence that adapts to diverse play-styles in a cooperative game, in what could be a win for human-AI teaming.
As artificial intelligence gets better at performing tasks once solely in the hands of humans, like driving cars, many see teaming intelligence as a next frontier. In this future, humans and AI are true partners in high-stakes jobs, such as performing complex surgery or defending from missiles. But before teaming intelligence can take off, researchers must overcome a problem that corrodes cooperation: humans often do not like or trust their AI partners.
Now, new research points to diversity as being a key parameter for making AI a better team player.
Article by @bdtechtalks
Mitigating ESG risk in AI systems through AI quality
“Quality is never an accident. It is always the result of intelligent effort” – John Ruskin
The adoption of artificial intelligence (AI) is gathering pace. Although highest in the tech, telecoms, financial services, and manufacturing sectors. And with a significant level of adoption in emerging markets, the trend has seen an increase in almost every industry, encompassing a range of business sectors from production, through marketing and sales to HR and risk management.
Alongside this trend, companies are broadening their focus to include stakeholders beyond their shareholders. This can be attributed to a variety of factors. The covid-19 pandemic has shone a critical light on longstanding inequalities in the area of employment opportunities, while the global movements against climate change crisis and in favor of social justice have exerted pressure on companies to create long-term, sustainable value with benefits extending not only to customers and employees but also to the wider community and to citizens at large.
Article by @VentureBeat
Could machine learning and operations research lift each other up?
Is deep learning really going to be able to do everything?
Opinions on deep learning’s true potential vary. Geoffrey Hinton, awarded for pioneering deep learning, is not entirely unbiased, but others, including Hinton’s deep learning collaborator Yoshua Bengio, are looking to infuse deep learning with elements of a domain still under the radar: operations research, or an analytical method of problem-solving and decision-making used in the management of organizations.
Machine learning and its deep learning variety are practically household names now. There is a lot of hype around deep learning, as well as a growing number of applications using it. However, its limitations are also becoming better understood. Presumably, that’s the reason Bengio turned his attention to operations research.
Article by @wef
There is no better time for fintech banking solutions.
As well as fintech, large digital ecosystems beyond banking are emerging in e-commerce, food tech, cab aggregation, payments, among others.
The year 2021 was particularly spectacular for the Indian startup ecosystem, which saw 42 companies enter the coveted unicorn club. Twelve of these companies were from the financial technologies sector, the single largest sector amongst unicorns born during the year.
This statistic demonstrates two facts: first, there is considerable potential in the financial services sector to deliver value by embracing digital and second, the time to do so is now.
Article by @VentureBeat
By 2025, the World Economic Forum estimates that 97 million new jobs may emerge as artificial intelligence (AI) changes the nature of work and influences the new division of labor between humans, machines and algorithms.
Specifically in banking, a recent McKinsey survey found that AI technologies could deliver up to $1 trillion of additional value each year. AI is continuing its steady rise and starting to have a sweeping impact on the financial services industry, but its potential is still far from fully realized.
The transformative power of AI is already impacting a range of functions in financial services including risk management, personalization, fraud detection and ESG analytics. The problem is that advances in AI are slowed down by a global shortage of workers with the skills and experience in areas such as deep learning, natural language processing and robotic process automation. So with AI technology opening new opportunities, financial services workers are eager to gain the skills they need in order to leverage AI tools and advance their careers.