Artificial Intelligence (AI) Startups Landscape


Source: Venture Scanner

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Startups Defining the Future of Retail


Source: CB Insights

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Keywords Associated with Robotics


Onalytica analyzed tweets and blogs from the top 100 Robotics influencers and brands between 1st January – 22nd November 2016 and counted the number of times various topics were mentioned in the context of Robotics.

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The Deep Learning Market: $1.7 Billion in 2022



The deep learning market is expected to be worth $1,722.9 Million by 2022, growing at a CAGR of 65.3% between 2016 and 2022. The deep learning market has a huge potential across various industries such as advertisement, finance, and automotive. The major factors driving the deep learning market globally are the robust R&D for the development of better processing hardware and increasing adoption of cloud-based technology for deep learning.

The market for the data mining application is expected to grow at the highest rate between 2016 and 2022

The deep learning market for data mining application is expected to grow at the highest CAGR between 2016 and 2022. The increasing usage of deep learning in data analytics, cyber security, fraud detection, and database systems is fueling the growth of data mining applications in the deep learning market. Medical industries generate huge amounts of data sets related to medication, patient details, and diagnosis. This data is converted into valuable patterns and is used to forecast future trends. Thus, data mining is expected to witness the highest growth rate in the medical industry.

Deep learning hardware market expected to grow at the highest rate between 2016 and 2022

The high growth rate of the hardware market for deep learning is attributed to the growing need for hardware platforms with a high computing power to run deep learning algorithms. There is increasing competition among established as well as startup players, leading to new product developments including both hardware development and software platforms to run deep learning algorithms and programs. For instance, Graphcore (a U.K.-based company) is developing the intelligent processing unit (IPU) for machine learning technology for use in applications from driverless cars to cloud computing. Some of the companies involved in the development of hardware for the deep learning technique are Google, Inc. (U.S.), Microsoft Corporation (U.S.), Intel Corporation (U.S.), Qualcomm, Inc. (U.S.), IBM Corporation (U.S.), and others.

North America leads the deep learning market in terms of market size

North America is currently leading the deep learning market and is projected to be in the leading position for the next few years owing to the wide adoption of deep learning technology. The growth of the deep learning market in North America is attributed to the high government funding, presence of leading players, and strong technical base.

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Augmented Intelligence (#AI)


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Look Ma, No Drivers: Transportation Automation


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The Role of Data in Statistical Modeling and Machine Learning

Statistical modeling.jpg

Oliver Schabenberger, EVP and CTO, SAS, on The difference between Statistical Modeling and Machine Learning:

We can… distinguish statistical modeling, classical machine learning and modern machine learning by the role of the data.

In statistical modeling, the data guide us to the selection of a stochastic model which serves as the abstraction for making probabilistic statements about questions of interest, such as hypotheses, predictions and forecasts.

In classical machine learning, the data drive the selection of the analytic technique to best perform the task at hand. The data trains the algorithms.

In modern machine learning, the data drive systems based on neural nets that self-determine the regularities in the data in order to learn a task. The process of training the neural network on the data learns the task. As someone put it, “The data does the programming.”

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