💯 The AI Platform Startup Ecosystem is Getting Crowded
Educational newsletter that discusses impactful ML research papers, cool tech releases, the money in AI, and real-life implementations
📝 Editorial
Artificial intelligence (AI) has been a very atypical technology trend. Traditionally, in emerging technology markets, startups disrupt incumbents from previous technology cycles until they become the new incumbents in the space. In AI, a lot of the innovation in recent years has not been driven by startups but by the research labs of technology giants such as Microsoft, Google, Amazon and Facebook. Those companies have created some of the top AI platform offerings in the market and have also been actively acquiring many early-stage AI startups so that they can increase their pool of data science talent. All those factors have made it incredibly difficult for startups in the AI space to achieve meaningful traction. However, that’s slowly changing.
After some struggle, some areas of the AI market are steadily showing a strong presence of well-capitalized startups. Areas such as interpretability, data labeling and model monitoring seem to be leading the pack. Just this week, startups such as Anomalo (data validation), V7 (data training) and Truera (explainability) raised sizable funding rounds, adding three more relevant companies to a highly competitive field. Whether those fields remain standalone markets or become features of broader AI platforms remains to be seen. However, for now, the native complexities of the AI space, together with this proliferation of well-capitalized startups, make it very difficult for data scientists and companies to keep up with the overall market. Despite the complexity and market fragmentation, the increasing number of AI platform startups are certainly pushing the boundaries of innovation in many segments of the market and finally challenging some of the incumbent platforms. Keeping up with the AI tech market can be both overwhelming and fascinating. Thankfully, there is a newsletter that can help 😉
🗓 Next week in TheSequence Edge:
Edge#49: an introduction to time-series forecasting models; how Uber uses neural networks to forecast during extreme events; Uber’s M3 time-series platform.
Edge#50: a deep dive into HiPlot and Polygames, two unique initiatives recently open-sourced by Facebook Research, that focus on advancing deep learning research with a specific focus on the PyTorch ecosystem.
Now, let’s review the most important developments in the AI industry this week.
🔎 ML Research
Privacy in Large Language Models
Several AI powerhouses such as Google, OpenAI, Apple and Stanford University collaborated in a new research study that shows some concerning security vulnerabilities in large language models such as GPT-3 ->read more on Google Research blog
Using Reinforcement Learning for ML Compiler Optimizations
Google Research published a paper detailing a technique that uses graph neural networks and reinforcement learning to optimize tasks in ML compilers ->read more on Google Research blog
Computer Vision in Small Devices
Microsoft Research published a paper presenting RNNPool, a pooling operator that reduces the size of image representations, enabling computer vision models that can run in devices with small memory and computational resources->read more on Microsoft Research blog
🤖 Cool AI Tech Releases
TensorFlow 2.4
TensorFlow has released its new update with features that were much required ->read more on TensorFlow blog
💬 Funny Tweet
💸 Money in AI
Data intelligence platform BigID raised $70 million in Series D on a valuation of $1 billion. The company claims to be the first to combine ML-based classification, cataloging, correlation, and cluster analysis to provide unmatched insight across legacy and cloud data stores, thereby unlocking clients’ data value for data privacy, security, and governance. Hiring.
AI-in-Sensors processors company AIStorm raised $16 million. The company creates high-performance processors that offer significant advantages for AIoT edge computing. Its unique approach is in the technology that allows the sensor to couple directly to popular convolutional neural networks.
AI explainability platform Truera raised $12 million in Series A. Their tools allow one to look into model predictions and gain insights into behavior, that way improving development and operationalization. Model-agnostic, Truera works with all types of regression and classification models, including logistic regression, gradient-boosted and other tree ensemble models, and deep neural networks.
ML-powered creative toolkit RunwayML raised $8.5 million in Series A. They use deep learning techniques to bring a new paradigm to content creation with synthetic media and automation.
Data validation startup Anomalo raised $5.95 million in venture capital. The company differentiates itself from competitors by using its own machine learning tools that let developers customize data validation and set rules that differentiate the company.
Data training computer vision platformV7 Labs raised $3 million in funding. It offers a complete toolkit for creating robust computer vision AI, maintaining state-of-the-art performance at every step. The company claims that their V7 Darwin leverages automation to create pixel-perfect ground truth for neural networks 10x faster than other tools.
TheSequence is a summary of groundbreaking ML research papers, engaging explanations of ML concepts, exploration of new ML frameworks, and platforms. It also keeps you up to date with the news, trends, and technology developments in the AI field.
5 minutes of your time, 3 times a week– you will steadily become knowledgeable about everything happening in the AI space.
