The Microsoft AI Powerhouse
Weekly newsletter that discusses impactful ML research papers, cool tech releases, the money in AI, and real-life implementations
📝 Editorial
Within the last few years, Microsoft’s transformation under Satya Nadella has been one of the best stories of technological innovation in corporate America. And artificial intelligence (AI) has been at the center of it. AI has become one of the trends that catalyzed the adoption of Microsoft technologies.
Microsoft’s impressive success in AI has different building blocks. Microsoft Research has been advancing AI research in very diverse areas. The Azure platform has been powering native services to enable AI capabilities, boosting a strong developer and partner communities. Microsoft remains competitive in AI markets like digital assistants and gaming. And the company has been incredibly smart about acquisitions in the AI market.
During its Ignite conference this week, Microsoft unveiled a series of new capabilities for several of its AI services. The Azure Cognitive Services API launched the general availability of new services in areas such as anomaly detection and spatial analysis. Azure ML presented a brand new user interface to streamline the lifecycle of machine learning models. Microsoft also unveiled Power Automate Desktop, developed atop of robotic process automation (RPA) startup Softmotive, its recent acquisition. Other services such as the Azure Bot also launched new capabilities, e.g. enhancements to Bot Framework Composer. When you put the Azure platform’s reach and depth of AI solutions together with the amazing wave of AI open-source releases from its research unit, as well as smart strategy of acquisitions, Microsoft starts looking like a front runner in the AI race.
Do you agree? How would you compare Microsoft against Amazon and Google in terms of AI offerings and capabilities? Use the comments section to share your thoughts. Share this post to get the discussion going.
🔺🔻TheSequence Scope – our Sunday edition with the industry’s development overview – is free. To receive high-quality educational content every Tuesday and Thursday, please subscribe to TheSequence Edge 🔺🔻
🗓 Next week in TheSequence Edge:
Edge#25: the concept of representation learning; an overview of Microsoft Research’s paper about representation and multi-task learning in language; Facebook’s fastText framework.
Edge#26: the concept of self-supervised learning; an overview of the self-supervised method for image classification proposed by Facebook; Google’s SimCLR framework for advancing contrastive self-supervised learning.
Now, let’s review the most important developments in the AI industry this week.
🔎 ML Research
Efficient NLP with Minimum Size
A paper by Google Research proposing a method that can achieve excellent performance in text classification tasks with a minimum model size ->read more on Google Research blog
A Dataflow Approach to Conversational AI
Microsoft Research presented a paper about a representation framework for modeling dialogues as graphs, demonstrating a new approach to conversational AI ->read more on Microsoft Research blog
The Magic Behind the New Alexa Features
The Amazon Research team has been publishing a series of insightful posts about the AI behind the new Alexa’s capabilities ->read more in their posts about speaking style adaptation, natural turn talking and interactive teaching
🤖 Cool AI Tech Releases
Dynabench
Facebook AI Research (FAIR) unveiled Dynabench, a platform that takes a new approach to data collection and benchmarking for AI models ->read more on the FAIR team blog
Updates and releases from Microsoft
Microsoft announced updates to its Azure ML and Azure Cognitive Services platforms ->read more in this analysis from VentureBeat. Microsoft also released InnerEye Deep Learning Toolkit as open-source software, which allows the training of high-performance models for medical imaging ->read more on Microsoft Blog
KILT
Facebook AI Research (FAIR) open-sourced KILT, a new set of datasets and benchmarks for knowledge-intensive language tasks ->read more on FAIR blog
GPT-3 for Microsoft
OpenAI licensed GPT-3 technology to Microsoft for its own products and services. GPT-3 still remains in limited data for researchers and partners ->read more on OpenAI blog
Google Cloud AI Platform
Google announced the general availability of its AI Platform Prediction service based on the Google Kubernetes Engine->read more on Google Cloud blog
💬 Useful Tweet
Data Scientist Job Market Analysis on KDNuggets
💸 Money in AI
Data intelligence company Ripjar secured $36.8 million in its Series B funding. The team creates analytical tools augmented with machine learning and artificial intelligence, which help alarm and protect global companies and governments from the threat of money laundering, fraud, cyber-crime, and terrorism.
Connected car analytics startup Aurora Labs has raised $23 million in Series B funding. They claim to reinvent software management for connected cars, calling it Self-Healing Software. Its proprietary machine learning algorithms analyze risks and changes in cars’ software functionality and behavior, lowering costs of software diagnostics and updates. All its management is done remotely.
No-code platform EasySend has raised $11 million in Round A funding. It is built for insurance companies and other regulated businesses to build out forms and other interfaces that take in customer information and subsequently use AI systems to process it more efficiently.
Context analytics startup Spectrum Labs has raised $10 million in a Series A round. The team develops tools that use contextual AI and automation to track and stop toxic behavior, such as hate speech, harassment, bullying, and others, across multiple languages in real-time.
Model and data monitoring startup WhyLabsraised $4 million in its seed round, coming out of stealth mode. Its proprietary platform is built to help data scientists monitor and troubleshoot problems they usually run into with datasets or AI models.
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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.
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