Intel Acquisition of Mobileye

March 13, 2017

Intel CEO Explains Why They're Buying Mobileye

Brian Krzanich, Intel's chief executive officer, explains why the company is buying Mobileye for about $15 billion. He speaks with Bloomberg's Cory Johnson on "Bloomberg Markets."

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13-Mar-2017

Intel Corp. (INTC)

Acquisition of Mobileye N.V. by Intel Corp Call

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Intel Acquisition of Mobileye

芯片巨头英特尔公司同意以153亿美元（总计202亿美元）的价格收购以色列无人驾驶汽车技术公司Mobileye。这一收购交易将会使来自美国的这家芯片制造商与自己的对手Nvidia、高通公司在全球汽车市场展开无人驾驶系统方面的直接竞争。

Intel to Acquire Mobileye

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Combining Technology and Talent to Accelerate the Future of Autonomous Driving

NEWS HIGHLIGHTS

 Brings together Mobileye’s leading computer vision expertise with Intel’s high- performance computing and connectivity expertise to create automated driving solutions from cloud to car.

 Positions Intel to accelerate innovation for automakers and to capture fast-growing market opportunity, estimated to be up to $70 billion by 2030.

 Acquisition expected to be accretive to Intel’s non-GAAP EPS and free cash flow immediately.

SANTA CLARA, Calif., and JERUSALEM, March 13, 2017 – Intel Corporation (NASDAQ: INTC) and Mobileye N.V. (NYSE: MBLY) today announced a definitive agreement under which Intel would acquire Mobileye, a global leader in the development of computer vision and machine learning, data analysis, localization and mapping for advanced driver assistance systems and autonomous driving. Pursuant to the agreement, a subsidiary of Intel will commence a tender offer to acquire all of the issued and outstanding ordinary shares of Mobileye for $63.54 per share in cash, representing an equity value of approximately $15.3 billion and an enterprise value of $14.7 billion.

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Long-term Planning by Short-term Prediction

Shai Shalev-Shwartz Nir Ben-Zrihem Aviad Cohen Amnon Shashua

Mobileye

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Abstract

We consider planning problems, that often arise in autonomous driving applications, in which an agent should decide on immediate actions so as to optimize a long term objective. For example, when a car tries to merge in a roundabout it should decide on an immediate acceleration/braking command, while the long term effect of the command is the success/failure of the merge. Such problems are characterized by continuous state and action spaces, and by interaction with multiple agents, whose behavior can be adversarial. We argue that dual versions of the MDP framework (that depend on the value function and the Q function) are problematic for autonomous driving applications due to the non Markovian of the natural state space representation, and due to the continuous state and action spaces. We propose to tackle the planning task by decomposing the problem into two phases: First, we apply supervised learning for predicting the near future based on the present. We require that the predictor will be differentiable with respect to the representation of the present. Second, we model a full trajectory of the agent using a recurrent neural network, where unexplained factors are modeled as (additive) input nodes. This allows us to solve the long-term planning problem using supervised learning techniques and direct optimization over the recurrent neural network. Our approach enables us to learn robust policies by incorporating adversarial elements to the environment.

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