World economy set to demystify AI technology
Despite downslide, AI models should be treated as features and not as bugs
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While many of the advancements in AI technology are coming from the west, Cathy Li, the Head of AI, Data, and Metaverse, believes that the first-mover advantage doesn't always guarantee the best position.
India's promising standing in the artificial intelligence (AI) landscape has garnered optimism from Cathy Li.
Speaking on the idea during the WEF summit, behind setting up the AI Governance Alliance, Li said that the primary objective was to demystify the technology, explaining it to the general public, but more importantly, to bring practitioners and policymakers together.
Li added that the goal is "to understand how we can responsibly design, develop, and deploy the technology in a manner that is both responsible and human-centered."
One of the most discussed issues regarding AI is its impact on jobs. However, Li said that AI is not the first technology to disrupt the labour market.
AI models are seen as a downside, but we should consider them not as bugs but as features. Think about models that can help foster creativity and tailor roles that can be complemented by such technology within the current boundaries.
After a breakthrough year for generative artificial intelligence, investors are looking for signs that new deep learning tools and techniques are filtering through to more industries. The shift from the excitement phase into the deployment phase is expected to continue in 2024, eventually helping to raise global productivity and potentially helping address challenges coming from unfavorable demographics in some countries, according to Goldman Sachs Asset Management. However even as investors seek out innovation that can drive earnings growth, discernment will be critical in 2024. “We are in an era of wider dispersion between high- and low-quality growth companies,” according to Goldman Sachs Asset Management’s 2024 outlook report.
The semiconductor makers and companies that produce equipment for semiconductor manufacturing — the hardware underlying the entire AI buildout — are in focus, it writes. Capital expenditure on the most advanced equipment used to produce semiconductors is growing rapidly. This is driven by both advancements in AI, which necessitate new chip designs, and reshoring of semiconductor production by developed countries to help the resilience of their supply chains.
Recent AI advances and growing adoption of cloud computing, meanwhile, are fuelling demand for increasingly advanced data centers. On the software side, enterprise spending on digitalization continues to increase.
Cybersecurity companies are also adopting cutting-edge AI techniques to automate the identification of potential threats and real-time response to security incidents. “Digital attacks are becoming increasingly sophisticated, frequent, and damaging.
Healthcare is an industry to watch given AI’s potential to transform complex biological data into meaningful insights, with potential implications for drug development, medical technology, and digital healthcare. AI algorithms can distinguish real heart attacks from false alarms with astonishing accuracy. An AI-powered smart implant for knee procedures can detect patients’ motion post-surgery, delivering real-time recovery insights to medical staff.
This has resulted in the most compelling AI-related investment opportunities in drug development for precision medicine, tech-enabled procedures and digital healthcare.
In the meantime, Goldman Sachs Asset Management sees several additional points for investors to consider when it comes to AI:
A disconnect between where most investors are positioned and the best potential opportunities is also seen. Investors, who look to complement their existing exposure to mega-cap US technology companies with allocations to other, often less well-known, technology firms, may be able to access secular winners that are relatively underappreciated by the broader market.
When it comes to introducing new technology into businesses models, tech as a standalone thesis isn’t sufficient to drive returns. Firms must implement the right processes, structures, and frameworks to exploit technology efficiently. For instance, identifying and implementing specific use cases for generative AI that will drive business growth will hold the key in driving potentially strong returns on investment.
And finally, AI is poised to support investors because of its capacity to process vast amounts of information quickly and accurately. This helps them to take more informed decisions by detecting trends and patterns, including data relationships that may be difficult, or even impossible, for humans to identify.
It will become increasingly important for investors to leverage new AI techniques to systematically extract information from data to inform investment decisions, particularly in public equity markets.
