Why Microsoft’s self-driving auto technique is quietly good
The self-driving car startup Cruise received more than $ 2 billion in a new round of investments from Microsoft, General Motors, Honda and institutional investors on Tuesday, according to a joint statement by Cruise, its owner GM and Microsoft. The investment will bring Cruise’s valuation to $ 30 billion and make Microsoft an official partner.
Per Tuesday’s announcement: “To unlock the potential of cloud computing for self-driving vehicles, Cruise will use Azure, Microsoft’s cloud and edge computing platform, to market its unique autonomous vehicle solutions on a large scale. As Cruise’s preferred cloud provider, Microsoft will also leverage Cruise’s extensive industry expertise to leverage its customer oriented Product innovation and support for transportation companies around the world through continued investment in Azure. “
So Cruise will be given the much-needed funding to research and gain (possibly discounted) access to Microsoft’s cloud computing resources and move closer to the goal of bringing a purpose-built self-driving car to market.
However, in the long run, Microsoft will benefit more from the business. It will not only win two very lucrative customers for its cloud business (Azure will also be GM’s preferred cloud provider according to the announcement), but also the “comprehensive industry expertise of Cruise” in the broader context of Microsoft’s self-driving car strategy. will potentially give Microsoft a solid foothold in the future of the still volatile self-driving auto industry.
At a time when most large tech companies are interested in purchasing self-driving autostarts or launching their own initiatives, Microsoft’s hands-off approach can ultimately become an industry leader.
Self-driving cars from the perspective of the AI business
Self-driving cars can be viewed as a special case of Artificial intelligence business. Any company that works with AI algorithms – namely machine learning – needs to bring some key elements together to have a viable business model:
- Algorithms: The company must either use existing machine learning algorithms or explore new architectures that suit the problem.
- Data: The company must have a solid infrastructure that consolidates different data sources. There must also be opportunities to collect and store new data from customers in order to continue to maintain and optimize the models and to secure the competitive advantage.
- Calculate resources: The company needs access to large computer clusters and specialized hardware to train and update its machine learning models and to deploy cloud-based inference on a large scale.
- talent: The company needs data scientists, data engineers, and machine learning engineers to develop and maintain AI models and explore new techniques.
Microsoft already has a solid AI stack and a full range of products that fit into this category. For example the company The Computer Vision Service is based on machine learning models developed by the company’s engineers. The models were trained using the company’s extensive image database. When customers use the AI service, they generate more data and labels to further improve the machine learning models. Finally, Microsoft’s Azure cloud has special hardware that enables the models to be both trained and deployed on a large scale and in a cost-effective manner.
Many companies use Microsoft’s Cognitive Services APIs to integrate AI capabilities into their applications.
Microsoft can also get involved in any type of venture that builds on this AI stack, such as: B. launching your own end-to-end computer vision applications or hosting advanced platforms for natural language processing such as GPA-3 from OpenAI.
When it comes to self-driving cars, however, some new components are added to the mix:
- Autonomous driving hardware: The company needs to develop lidars, sensors, and cameras other hardware that enables self-propelled functions.
- vehicle: The company must either manufacture its own vehicle or find a manufacturing partner to integrate the self-driving vehicle equipment.
Self-driving cars present new manufacturers and legal challenges that would challenge a company that is primarily in the software business. There are a few ways companies can address these challenges.
How does Microsoft’s self-driving car strategy compare to others?
The traditional way to enter an emerging market is to either build it yourself or to buy it from someone else.
Google developed its own self-driving car lab in the late 2000s, which was later renamed Waymo. Waymo develops AI software and hardware for autonomous driving. Google doesn’t make its own cars and relies on vehicles from other automakers such as Toyota, Audi, Fiat Chrysler and Lexus to test and deploy its technology.
However, Google had a head start that allowed it to create its own self-driving car unit from scratch. Other companies that later entered the field made up for their delay by purchasing self-driving autostarts. Examples include Amazon takes over Zoox and Intel takes over MobileEye.
Tesla is among the few who have a complete stack of self-driving cars. The electric car manufacturer has autonomous driving technology integrated into its electric vehicles. There are also millions of cars sold that are constantly collecting new data to further improve their algorithms. Apple also has plans to manufacture it his own self-driving car, although full details are not yet known.
Microsoft’s approach to the self-driving auto industry is different.
“We work together across the industry. We are not able to manufacture vehicles or offer end-of-life mobility as a service, ”wrote Sanjay Ravi, general manager of the automotive industry at Microsoft, in one blog entry This set Microsoft’s automotive strategy in 2019.
Instead of buying startups and testing cars in cities, Microsoft is offering a self-driving auto-start support program by giving them technical support and discounted access to cloud services. These startups can become potential Microsoft partners in the future. In October, Microsoft signed a partnership agreement Wayve, a London-based developer of self-driving car software who was part of Microsoft’s launcher. Cruise is the second self-driving car company Microsoft is working with. Microsoft also has partnerships with several automobile manufacturers to provide them with cloud services.
Why Microsoft’s strategy can be successful
The problem with the self-driving auto industry is that we still don’t know when we will get there. Every year we miss new deadlines for fully autonomous cars on the road. But like that In the search for Artificial General Intelligence, we know we have a bumpy and potentially long road ahead of us.
We also don’t know what the final technology will look like. Elon Musk, CEO of Tesla, thinks so Computer vision alone will be enough to achieve full autonomy. Other companies are betting that lidar technology will become more affordable and stable in the future. Car design will also change as the industry matures.
Another problem is the regulations for self-driving cars. Are self-driving cars allowed to share the road with human drivers? Are they only allowed in certain geographically fenced off areas? How is culpability determined in the event of an accident?
Any of these areas can experience profound changes, and those changes will be critical in determining which startups will thrive and which will collapse in the years to come. Interestingly, data, cloud, and software are likely to remain constant, the three areas in which Microsoft already excels.
Because of this, Microsoft’s strategy of not acquiring startups protects the company from industry volatility.
On the one hand, partnership is a flexible format that is well suited to the rapidly developing self-driving parking lot. Partnering up is faster and less expensive than a full acquisition (compare the $ 2 billion partnership to a full $ 30 billion acquisition of Cruise, if it was at all possible). At the same time, getting out of a partnership is much easier than scrap and sell an entire self-propelled unit.
With small investments, Microsoft can build a broader network and embrace a variety of solutions through its self-driving startup accelerator program and partnership agreements. The list of startups included in Microsoft’s self-driving startup program already spans a wide variety of research areas and directions. Each of them can become groundbreaking solutions in the future. Microsoft supports these startups, but also uses their industry expertise and develops its own talents and tools. This is very important if Microsoft is considering a more serious step in self-driving car development.
As the field matures and potential winners become more apparent, Microsoft will be in a better position to expand its relationship with startups to a full partnership and eventual acquisition.
And if Microsoft’s partnership with OpenAI is an indication of this. Much of the investments Microsoft makes in startups is in Azure credits, which ensures that these startups are tied to Microsoft’s cloud service rather than partnering with other cloud providers.
On a larger scale, Microsoft will become a growing hub for self-driving autostarts through its wide range of partnerships. The know-how and experience of these startups will be used to improve cloud and AI services for autonomous driving and thus attract more customers.
Many analysts believe Microsoft is lagging behind in the self-driving car space because there is no active program to test cars in cities. I think the company has taken a wise move to solidify its position in things that stay constant (cloud, data and algorithms) while developing a strategy that will allow it to adapt to the inevitable changes that will overcome the industry in the future years.
This article was originally published by Ben Dickson on TechTalks, a publication that examines technology trends, how they affect the way we live and do business, and what problems they solve. But we also discuss the evil side of technology, the darker effects of the new technology, and what to look out for. You can read the original article here.
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Published on January 27, 2021 – 11:24 UTC