👾 The Monster C3.AI IPO
Weekly newsletter that discusses impactful ML research papers, cool tech releases, the money in AI, and real-life implementations
📝 Editorial
What a week we have had in US public markets. News outlets were inundated with headlines of DoorDash and Airbnb soaring record valuations on their opening day. Those noisy headlines managed to steal the spotlight from what can be considered, by many parameters, the most successful IPO of the week: C3.ai. In their public market debut, C3.ai stock raised 175% trading above $100 from an initial price of $42.
This is a newsletter about AI so what are we doing discussing an IPO?
Well, C3.ai is not just any IPO to us AI fanboys. It’s the first public offering of a standalone AI platform company and, therefore, a first-row seat for measuring the sentiment of public market investors with respect to the enterprise AI space. In a week inundated with interest in consumer market companies, C3.ai’s IPO can be considered a smashing success. The company also inherits the risk and benefits of being the first IPO in an enterprise tech trend. On one side, if you want exposure to a pure enterprise AI play, C3.ai is the only game in town. You can obviously bet on Google, Microsoft or Nvidia, but those stocks are not driven solely by their impact in the AI market. However, C3.ai stock also becomes incredibly vulnerable to announcements by their incumbent competitors such as AWS, Google or Microsoft. What I consider more important is that the C3.ai IPO opens the door for new AI startups planning to go public. My next IPO candidate: DataRobot.
Disclosure: I am a happy holder of C3.ai stock
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🗓 Next week in TheSequence Edge:
Edge#47: the concept of Energy-Based Models; OpenAI Research About Learning Concepts Using Energy Functions; AI Explainability 360 Toolkit.
Edge#48: OpenAI’s Deep Double Descent Hypothesis, which shows us when more data and bigger models can hurt performance.
Now, let’s review the most important developments in the AI industry this week.
🔎 ML Research
AutoML for Time Series Data
Google Research published a very insightful blog post about their progress using AutoML in time series models ->read more on Google Research blog
More Advancements in Pre-trained Language Models
Microsoft Research published a paper unveiling MPNet, a new pre-trained language model that combines some of the most advanced techniques in the space ->read more on Microsoft Research blog
IBM and MIT on Deep Learning Hacks
Researchers from IBM and MIT published a paper outlining a brain-inspired method to make deep learning models more resilient to adversarial attacks ->read more on IBM Research blog
🤖 Cool AI Tech Releases
SageMaker JumpStart
AWS unveiled SageMaker JumpStart, an interface for accessing and deploying pre-built machine learning models ->read more on the AWS team blog
BERT in TensorFlow Hub
Several versions of the famous Google BERT model are now available in the TensorFlow Hub ->read more on the TensorFlow blog
💬 Useful Tweet
💸 Money in AI
End-to-end enterprise AI platform DataRobot raised an additional $50 million, bringing it to $320 million at a $2.8 billion valuation. The platform allows customers to prepare their data, create and validate ML models, including time series models, and deploy and monitor those models in a single solution.
Relational database startup SingleStore raised $80 million in a funding round. They create an ecosystem that allows ML models to perform at their peak. Hiring.
Enterprise-ready feature store for ML Tecton.ai raised a $35 million in Series B. Their goal is to enable ML teams to build great features, serve them to production quickly and reliably, and do it at scale. Hiring.
Cloud data warehouse Firebolt raised $37 million in funding. Their promise is to deliver the most efficient and fastest analytics experience at any scale.
Model monitoring solution Arthur.ai raised a $15 million in Series A. Their solution protects the integrity of automated workflows and helps monitor all running in a single dashboard, no matter where they were deployed. Hiring.
Synthetic data generation startup Parallel Domain raised $11 million in Series A funding. The platform is able to generate data uninterrupted to accelerate the development of computer vision, especially in autonomous systems. Hiring.
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