Concerns about an impending fourth AI winter are rising as doubts emerge over whether artificial intelligence will deliver enough practical benefits to justify its high costs. Recent reports from Goldman Sachs and other research bodies highlight this skepticism. However, a solution has been present for some time—engineered intelligence, a concept emphasizing the practical application of AI through engineering principles.

The Missing Link in AI Development

Traditionally, scientific breakthroughs in fields like chemistry and physics are first made in laboratories and then transferred to engineers to develop real-world applications. This process ensures that discoveries are turned into practical solutions. However, AI lacks a similar transition mechanism. Instead of a dedicated discipline for applied AI, organizations often hire data scientists—primarily researchers—to work on developing practical AI solutions. This mismatch has contributed to a significant failure rate, with 87% of AI projects not reaching successful outcomes.

What is Engineered Intelligence?

Engineered intelligence, or intelligence engineering, is emerging as a new field focusing on applying AI research in practical settings, much like how chemical or mechanical engineers utilize scientific discoveries. This discipline allows experts, scientists, and engineers to develop intelligent solutions without needing to become data scientists. By reestablishing research-to-engineering pipelines and forming partnerships with academic institutions and technology vendors, industrial organizations are setting the stage for engineered intelligence. This approach mirrors how breakthroughs in other scientific areas are handed off to specialized engineers.

The Benefits of Intelligence Engineering

With intelligence engineering, AI research can be transformed into breakthrough applications, yielding tangible value and producing outcomes that might not have been identified by data scientists alone. The process facilitates creating value-driven AI solutions that are feasible and safe for production use, contributing to meaningful advancements across various industries.

Implementing Intelligence Engineering: A Five-Step Approach

To introduce intelligence engineering into an organization, practical experience is crucial. Here’s a five-step guide that differs from traditional AI implementation methods:

1. Map Existing Expertise: Identify areas of expertise within current organizational processes.
2. Evaluate Expertise Value: Assess which expertise is most valuable and determine its scarcity or abundance.
3. Prioritize Expertise: Focus on the top five areas of valuable and scarce expertise.
4. Analyze for Feasibility and ROI: Examine these areas for potential return on investment, feasibility, cost, and timeline.
5. Invest in Execution: Select a subset of these valuable use cases and proceed with development.

A New Wave of AI-Driven Value Creation

By adopting intelligence engineering, organizations can expand their capabilities, moving beyond existing expertise to identify new opportunities. This approach enables safe and practical value creation, both within organizations and across broader ecosystems. As more industries and educational institutions develop programs focused on engineered intelligence, the resulting innovations will unlock unrealized economic and societal benefits, paving the way for new job categories and a surge in value creation.

Brian Evergreen, author of Autonomous Transformation: Creating a More Human Future in the Era of Artificial Intelligence, and Kence Anderson, author of Designing Autonomous AI, both emphasize the potential of engineered intelligence to redefine AI’s impact in practical, everyday applications.

The rise of “emotion AI,” which aims to equip artificial intelligence with the ability to understand human emotions, is becoming a notable trend in business software, according to PitchBook’s recent Enterprise SaaS Emerging Tech Research report. This technology is seen as a step beyond sentiment analysis, promising more nuanced interpretations of human interactions by using multimodal inputs like visual, audio, and psychological data. Despite its potential, the effectiveness and ethical implications of emotion AI remain questionable.

The concept behind emotion AI is straightforward: as businesses increasingly rely on AI for customer service, sales, and other interactions, these AI bots need to distinguish between different emotional cues, such as anger and confusion. Emotion AI intends to make AI assistants more human-like in their responses by analyzing various signals, from facial expressions to voice tones. Major cloud providers, including Microsoft and Amazon, already offer services with emotion AI capabilities, making these tools more accessible to developers.

Derek Hernandez, a senior analyst at PitchBook, notes the growing importance of emotion AI with the proliferation of AI assistants and automated human-machine interactions. Hernandez highlights the role of cameras, microphones, and wearable devices in capturing the necessary data for emotion detection. This growing interest has spurred investment in startups like Uniphore, MorphCast, Voicesense, and others, which focus on developing emotion AI technologies.

However, the push toward emotion AI brings with it significant challenges. Critics argue that the technology might be inherently flawed. Research published in 2019 suggests that human emotions cannot be accurately determined by facial movements alone, challenging the basic premise of emotion AI. Moreover, regulatory concerns, such as those outlined in the European Union’s AI Act, which restricts emotion detection in specific contexts, could limit its application. U.S. state laws like Illinois’ Biometric Information Privacy Act (BIPA) further complicate the use of biometric data without explicit consent.

The debate around emotion AI offers a glimpse into the potential future of AI in the workplace. While emotion AI could enhance customer service, sales, and HR tasks by making interactions more personalized and empathetic, it raises questions about privacy, ethical implications, and the actual effectiveness of such technology. As companies continue to embed AI across various aspects of business operations, the success and acceptability of emotion AI will likely depend on addressing these challenges.

San Francisco is emerging as the preferred destination for startups, including those outside the AI sector, thanks to its unparalleled concentration of tech talent and investor capital. Data shared exclusively with TechCrunch by VC firm SignalFire reveals that the San Francisco Bay Area houses 49% of all big tech engineers and 27% of startup engineers in the U.S., making it the largest tech employment hub in the country. Moreover, this region is home to 12% of the most prominent VC-backed founders and 52% of startup employees, reinforcing its status as a critical center for tech innovation and growth.

Despite narratives suggesting a decline in San Francisco’s tech scene, SignalFire partner Josh Constine argues otherwise, emphasizing that the city’s dominance has only increased, particularly in the wake of the recent AI boom. This resurgence is attracting international founders who see San Francisco as a vital ecosystem for scaling their ventures.

Founders like Daniel Lenton of Unify have relocated from cities like Berlin to San Francisco, citing the benefits of proximity to other tech startups and frequent interactions with potential partners and investors. Lenton, who secured $8 million in funding from investors including SignalFire and Microsoft’s M12 Capital, noted that while remote engagement with investors was possible, being physically present in San Francisco allowed for more spontaneous, collaborative opportunities, such as informal brainstorming sessions with other AI tech startups.

Similarly, Anh-Tho Chuong, co-founder and CEO of Lago, an open-source billing platform, has moved her company from Paris to San Francisco. Despite considering New York for its convenience, Chuong observed a revitalized tech scene in San Francisco, with numerous founders returning. Chuong emphasized the advantage of San Francisco’s concentrated talent and customer pool, which she believes provides better opportunities for hiring and networking compared to other cities.

The appeal of San Francisco lies not just in structured events but also in the serendipitous encounters that occur within its dense tech community. Chuong and Lenton both highlighted the value of these organic interactions, which often lead to collaboration and support. As Y Combinator partner Diana Hu puts it, San Francisco offers a unique environment where founders can “manufacture luck,” making it an attractive destination for startups looking to scale.