Introduction to Snorkel AI

Snorkel AI CEO Alex Ratner said his company is placing more emphasis on helping subject matter experts build datasets and models for evaluating AI systems. Alex Ratner, CEO of Snorkel AI, remembers a time when data labeling —the grueling task of adding context to swathes of raw data and grading an AI model’s response— was considered “janitorial” work among AI researchers. But that quickly changed when ChatGPT stunned the world in 2022 and breathed new life (and billions of dollars) into a string of startups rushing to supply human-labeled data to the likes of OpenAI and Anthropic to train capable models.

Shift in Data Labeling

Now, the crowded field of data labeling appears to be undergoing another shift. Fewer companies are training large language models from scratch, leaving that task instead to the tech giants. Instead, they are fine-tuning models and building applications in areas like software development, healthcare, and finance, creating demand for specialized data. AI chatbots no longer just write essays and haikus; they’re being tasked with high-stakes jobs like helping physicians make diagnoses or screening loan applications, and they’re making more mistakes. Assessing a model’s performance has become crucial for businesses to trust and ultimately adopt AI, Ratner said. “Evaluation has become the new entry point,” he told Forbes.

New Direction for Snorkel AI

That urgency for measuring AI’s abilities across very specific use cases has sparked a new direction for Snorkel AI, which is shifting gears to help enterprises create evaluation systems and datasets to test their AI models and adjust them accordingly. Data scientists and subject matter experts within an enterprise use Snorkel’s software to curate and generate thousands of prompt and response pairs as examples of what a correct answer looks like to a query. The AI model is then evaluated according to that dataset, and trained on it to improve overall quality.

Funding and Growth

The company has now raised $100 million in a Series D funding round led by New York-based VC firm Addition at a $1.3 billion valuation— a 30% increase from its $1 billion valuation in 2021. The relatively small change in valuation could be a sign that the company hasn’t grown as investors expected, but Ratner said it’s a result of a “healthy correction in the broader market.” Snorkel AI declined to disclose revenue.

Success Stories

Customer support experts at a large telecommunication company have used Snorkel AI to evaluate and fine-tune its chatbot to answer billing-related questions and schedule appointments, Ratner told Forbes. Loan officers at one of the top three U.S. banks have used Snorkel to train an AI system that mined databases to answer questions about large institutional customers, improving its accuracy from 25% to 93%, Ratner said. For nascent AI startup Rox that didn’t have the manpower or time to evaluate its AI system for salespeople, Snorkel helped improve the accuracy by between 10% to 12%, Rox cofounder Sriram Sridharan told Forbes.

Competition and Challenges

It’s a new focus for the once-buzzy company, which spun out of the Stanford Artificial Intelligence Lab in 2019 with a product that helped experts classify thousands of images and text. But since the launch of ChatGPT in 2022, the startup has been largely overshadowed by bigger rivals as more companies flooded the data labeling space. Scale AI, which also offers data labeling and evaluation services, is reportedly in talks to finalize a share sale at a $25 billion valuation, up from its $13.8 billion valuation a year ago. Other competitors include Turing, which doubled its valuation to $2.2 billion from 2021, and Invisible Technologies, which booked $134 million in 2024 revenue without raising much from VCs at all.

Differentiation and Future Plans

Snorkel has faced macro challenges too: As AI models like those powering ChatGPT got better, they could label data on a massive scale for free, shrinking the size of the market further. Ratner acknowledged that Snorkel saw a brief period of slow growth right after OpenAI launched ChatGPT and said enterprises had paused pi

Introduction to Supply Chain Vulnerabilities

Nothing is more vulnerable than supply chains – everything and anything can rock them without notice. Tariffs, weather events, political disruptions, economic issues, worker shortages, and epidemics will always disrupt even the smoothest-flowing chains. Let’s not even get started on the 2020 Covid toilet-paper crisis. And we’re seeing the potential pain Apple is facing with tariffs on its manufacturing operations in China.

The Potential of Autonomous Supply Chains

Could self-managing, autonomous supply chains help companies rapidly adjust to such disruptions? Should they? A new survey of 1,000 C-suite executives out of Accenture says supply chains are the new untamed frontier for artificial intelligence. “Today, companies operate their supply chains mostly manually,” the Accenture report’s co-authors, Max Blanchet, Chris McDivitt, and Stephen Meyer, stated. “Such supply chains aren’t prepared to handle sudden disruption such as the recent tariff announcements.”

Limitations and Opportunities of AI in Supply Chains

Of course, no AI can predict political actions or natural disasters. But it can play a role in making it easier to switch off one supply route and switch on another. At this time, few executives in the Accenture’s survey currently have autonomy built into their supply chains – the average company’s supply chain is only 21% autonomous. “Few companies use AI to adjust sourcing strategies, reroute logistics and recalibrate inventory positions with minimal human intervention," the report states.

Current State of Autonomous Supply Chains

Only 25% of companies indicated that autonomous supply chains were a key priority for them. Only a very small fraction, four percent, aspired to reach full autonomy. Advancing autonomy in supply chains is “held back by concerns like data privacy, poor data quality, immature processes, and low trust in AI.”

Overcoming Challenges to Achieve Autonomy

There are two tall orders for achieving greater autonomy in supply chains. First, start with shattering functional silos, the researchers advise. “Autonomous decision-making requires unprecedented transparency across functions, processes and dependencies. Without end-to-end visibility, even the most sophisticated AI systems will fail to deliver meaningful value.” Processes also need to be simplified. “Companies that streamline operations and standardize processes will scale technology faster, adapt more quickly and accelerate AI learning cycles.”

Future of Autonomous Supply Chains

We’re likely not likely to see significant progress in supply-chain autonomy for at least 10 years, the researchers predict. By then, approximately 40% aspire to achieve a higher degree of autonomy where the system handles most operational decisions.

Characteristics of Autonomous AI-Powered Supply Chains

What does an autonomous AI-powered supply chain look like? Current automated systems "follow pre-set instructions and require human oversight – think of the cruise control function in a typical car," the Accenture team explained. “Autonomous systems include a degree of automation but go beyond it. They are enabled by AI agents that make decisions and perform tasks without human intervention.”

Benefits of Autonomous Supply Chains

Most executives agree that autonomous supply chains can deliver tangible advantages. Survey respondents expect a 5% increase in net income and 7% improvement in return on capital employed. Operationally, companies could slash order lead times by 27%, and boost productivity by 25%. Survey respondents believe autonomous supply chains to shorten the time it takes them to react to shocks by at least 62%, and recover from disruption 60% faster compared to today’s existing networks.

Recommendations for Business Leaders

The Accenture team advises business leaders to “build solid data foundations through a secure digital core, which standardizes platforms and governance frameworks.” Companies should also “invest strategically in AI-enabling technologies, starting with targeted pilots before scaling proven solutions.” Most importantly, they need to “restructure how people and technology collaborate, shifting human roles from routine execution to strategic guidance and oversight.”

Conclusion

In conclusion, autonomous supply chains have the potential to revolutionize the way companies manage their supply chains, enabling them to respond quickly to disruptions and improve their overall efficiency. While there are challenges to overcome, the benefits of autonomous supply chains make them an attractive option for businesses looking to stay ahead of the curve.

FAQs

Q: What is the current state of autonomy in supply chains?
A: The average company’s supply chain is only 21% autonomous, with few companies using AI to adjust sourcing strategies, reroute logistics, and recalibrate inventory positions with minimal human intervention.
Q: What are the benefits of autonomous supply chains?
A: Autonomous supply chains can deliver tangible advantages, including a 5% increase in net income, 7% improvement in return on capital employed, and operational improvements such as reduced order lead times and increased productivity.
Q: How can businesses achieve autonomy in their supply chains?
A: Businesses can achieve autonomy by shattering functional silos, simplifying processes, building solid data foundations, investing in AI-enabling technologies, and restructuring how people and technology collaborate.
Q: What is the predicted timeline for significant progress in supply-chain autonomy?
A: Significant progress in supply-chain autonomy is not expected for at least 10 years, with approximately 40% of companies aspiring to achieve a higher degree of autonomy by then.

Leveraging AI and automation for impact is transforming industries, but it raises important questions about the moral implications of machine learning. As AI becomes increasingly pervasive, it’s essential to consider the ethics of AI and its potential consequences on society. In this comprehensive guide, we’ll explore the moral implications of machine learning and provide a framework for responsible AI development.

Understanding AI and Machine Learning

AI refers to the development of computer systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. Machine learning is a subset of AI that involves training algorithms on data to enable them to make predictions or take actions without being explicitly programmed. As AI and machine learning continue to advance, they are being applied in various domains, including healthcare, finance, transportation, and education.

Types of Machine Learning

There are several types of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training algorithms on labeled data to enable them to make predictions on new, unseen data. Unsupervised learning involves training algorithms on unlabeled data to identify patterns or relationships. Reinforcement learning involves training algorithms to take actions that maximize a reward or minimize a penalty.

Applications of Machine Learning

Machine learning has numerous applications, including image recognition, natural language processing, and predictive analytics. In healthcare, machine learning can be used to diagnose diseases, develop personalized treatment plans, and improve patient outcomes. In finance, machine learning can be used to detect fraudulent transactions, predict stock prices, and optimize investment portfolios.

The Moral Implications of Machine Learning

As machine learning becomes increasingly pervasive, it raises important questions about the moral implications of AI. One of the primary concerns is bias in machine learning algorithms, which can result in discriminatory outcomes. For example, a machine learning algorithm used to predict creditworthiness may be biased against certain racial or ethnic groups.

Bias in Machine Learning

Bias in machine learning can occur due to various factors, including biased training data, flawed algorithm design, and inadequate testing. To mitigate bias, it’s essential to ensure that training data is diverse and representative, algorithms are designed to detect and correct bias, and testing is rigorous and comprehensive.

Transparency and Explainability

Another important concern is the lack of transparency and explainability in machine learning algorithms. As machine learning models become increasingly complex, it’s challenging to understand how they arrive at their predictions or decisions. To address this concern, it’s essential to develop techniques that provide insights into the decision-making process of machine learning algorithms.

Responsible AI Development

To ensure that AI is developed and deployed in a responsible manner, it’s essential to establish guidelines and regulations that prioritize transparency, accountability, and fairness. This includes establishing standards for data quality, algorithm design, and testing, as well as providing mechanisms for reporting and addressing bias and other ethical concerns.

Regulatory Frameworks

Regulatory frameworks are essential for ensuring that AI is developed and deployed in a responsible manner. This includes establishing standards for data protection, algorithm design, and testing, as well as providing mechanisms for reporting and addressing bias and other ethical concerns.

Industry Initiatives

Industry initiatives are also crucial for promoting responsible AI development. This includes establishing guidelines and best practices for AI development, providing training and education on AI ethics, and encouraging collaboration and knowledge-sharing among stakeholders.

Conclusion

In conclusion, the ethics of AI is a critical concern that requires careful consideration and attention. As AI and machine learning continue to advance, it’s essential to prioritize transparency, accountability, and fairness to ensure that AI is developed and deployed in a responsible manner. By establishing guidelines and regulations, promoting industry initiatives, and encouraging public awareness and engagement, we can mitigate the risks associated with AI and ensure that its benefits are realized.

Frequently Asked Questions

What is AI, and how does it work?

AI refers to the development of computer systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. AI works by using algorithms and data to enable machines to make predictions or take actions without being explicitly programmed.

What are the benefits of AI?

The benefits of AI include improved efficiency, enhanced decision-making, and increased productivity. AI can also help to solve complex problems, such as climate change, healthcare, and education.

What are the risks of AI?

The risks of AI include bias, job displacement, and cybersecurity threats. AI can also be used for malicious purposes, such as spreading misinformation or conducting cyber attacks.

How can we ensure that AI is developed and deployed responsibly?

To ensure that AI is developed and deployed responsibly, it’s essential to establish guidelines and regulations that prioritize transparency, accountability, and fairness. This includes establishing standards for data quality, algorithm design, and testing, as well as providing mechanisms for reporting and addressing bias and other ethical concerns.

What role can individuals play in promoting responsible AI development?

Individuals can play a crucial role in promoting responsible AI development by staying informed about AI ethics, participating in public debates and discussions, and advocating for policies and regulations that prioritize transparency, accountability, and fairness. Individuals can also support organizations that prioritize responsible AI development and promote industry initiatives that encourage collaboration and knowledge-sharing among stakeholders.

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