TL;DR:
- Dropbox Ventures and AWS launch initiatives to fund startups in the AI space, focusing on generative AI products.
- Significant investments are being made by Salesforce Ventures, Workday, Accenture, and PwC to support AI and machine learning startups.
- Meredith Whittaker highlights the opaque nature of AI systems, citing an example of creditworthiness determination based on scraped social media data.
- Whittaker emphasizes the need for structural change and warns about the potential loss of individual and collective agency in the face of AI progress.
Main AI News:
In the rapidly evolving AI industry, keeping pace can be a challenging task. Until an AI system takes over this responsibility, we bring you a concise summary of the latest developments in machine learning from the past week. Additionally, we highlight noteworthy research and experiments that have flown under the radar.
The competitive landscape in the realm of AI, particularly in generative AI, is scorching hot and only getting hotter. This week, Dropbox made its foray into the corporate venture fund scene by launching Dropbox Ventures. The fund will focus on nurturing startups that are building AI-powered products to shape the future of work. Not to be outdone, AWS countered with the debut of its own initiative, a $100 million program dedicated to funding generative AI endeavors led by its partners and customers.
Undoubtedly, vast sums of money are being funneled into the AI space. Salesforce Ventures, the venture capital arm of Salesforce, has announced plans to invest $500 million in startups focused on developing generative AI technologies. Workday recently injected an additional $250 million into its existing VC fund, with a specific emphasis on supporting AI and machine learning startups. Moreover, Accenture and PwC have revealed their ambitious investment plans of $3 billion and $1 billion, respectively, in AI.
However, one must ponder whether money alone can resolve the outstanding challenges in the field of AI. During a thought-provoking panel discussion at a Bloomberg conference in San Francisco, Meredith Whittaker, the president of the secure messaging app Signal, shed light on a concerning issue. She argued that the technological infrastructure behind some of today’s most buzzworthy AI applications is becoming alarmingly opaque. Whittaker provided an example of an individual walking into a bank to request a loan.
Little do they know that a system powered by a Microsoft API, leveraging scraped social media data, may deem them unworthy of credit. Whittaker remarked, “I’m never going to know [because] there’s no mechanism for me to know this.” Evidently, the problem lies not in the availability of capital but rather in the prevailing power hierarchy, according to Whittaker.
“I’ve been at the table for like 15 years, 20 years. I’ve been at the table. Being at the table with no power is nothing,” she continued, highlighting the need for structural change. However, effecting such change is a formidable task, especially when the resulting shifts may not favor those in power. Whittaker issued a warning about the potential consequences if there is insufficient resistance.
As progress in AI gains momentum, so do its societal impacts. We find ourselves hurtling down a “hype-filled road toward AI,” she cautioned, “where that power is entrenched and naturalized under the guise of intelligence and we are surveilled to the point [of having] very, very little agency over our individual and collective lives.”
Conclusion:
The AI industry is witnessing a surge in investment as major players like Dropbox, AWS, Salesforce Ventures, Workday, Accenture, and PwC pour substantial funds into the development of AI-powered solutions. However, the concerns raised by Meredith Whittaker regarding the opaqueness of AI systems and the potential erosion of personal agency highlight the need for a thoughtful approach to ensure the responsible and ethical deployment of AI technologies. As the market continues to evolve, addressing these challenges will be crucial to building trust and ensuring long-term sustainability in the AI landscape.
