The startups are separated into eight categories: Technologies, Sectors, Industrial, Automotive, Enterprise, Healthcare, Fintech, and Marketing, and the technology is being used for a myriad of purposes. In the following post, I'll dive deeper into analytics on Israel's AI startup ecosystem. For now, here's a top 10 summary of my findings: For the 2017 year-to-date, Israeli AI startups have raised $837 million, which is already larger than the total period for 2016, and represents a fifteen-fold increase in the last five years. Based on the available data, Israeli AI startups are exiting at an average multiple of 8.2 times their total funding, ranging from a low of 2.0 to a high of 20.0 times.

Many readers are unfamiliar with the history of Sputnik The effect of Soviet Union's achievement in launching the first man made satellite (i.e. Sputnik created the urgency to upgrade America's science and technology infrastructure: This was viewed by a shocked audience of over 200 million people. A vast majority of that audience was from countries were the game of Go is popularly played (i.e. To have a Western developed automation arrive and vanquish a legendary player like Lee Sedol certainly shocked the population to its core. Chinese authorities were concerned enough about the social ramifications that they hastily imposed a country-wide ban on the live-streaming of the event. This kind of shock of one's core view of the world is certainly to galvanize serious action.

Computing partition function is the most important statistical inference task arising in applications of Graphical Models (GM). Since it is computationally intractable, approximate methods have been used to resolve the issue in practice, where mean-field (MF) and belief propagation (BP) are arguably the most popular and successful approaches of a variational type. In this paper, we propose two new variational schemes, coined Gauged-MF (G-MF) and Gauged-BP (G-BP), improving MF and BP, respectively. Both provide lower bounds for the partition function by utilizing the so-called gauge transformation which modifies factors of GM while keeping the partition function invariant. Moreover, we prove that both G-MF and G-BP are exact for GMs with a single loop of a special structure, even though the bare MF and BP perform badly in this case. Our extensive experiments, on complete GMs of relatively small size and on large GM (up-to 300 variables) confirm that the newly proposed algorithms outperform and generalize MF and BP.

We generalize a support vector machine to a support spinor machine by using the mathematical structure of wedge product over vector machine in order to extend field from vector field to spinor field. The separated hyperplane is extended to Kolmogorov space in time series data which allow us to extend a structure of support vector machine to a support tensor machine and a support tensor machine moduli space. Our performance test on support spinor machine is done over one class classification of end point in physiology state of time series data after empirical mode analysis and compared with support vector machine test. We implement algorithm of support spinor machine by using Holo-Hilbert amplitude modulation for fully nonlinear and nonstationary time series data analysis.

We consider the topic of multivariate regression on manifold-valued output, that is, for a multivariate observation, its output response lies on a manifold. Moreover, we propose a new regression model to deal with the presence of grossly corrupted manifold-valued responses, a bottleneck issue commonly encountered in practical scenarios. Our model first takes a correction step on the grossly corrupted responses via geodesic curves on the manifold, and then performs multivariate linear regression on the corrected data. This results in a nonconvex and nonsmooth optimization problem on manifolds. To this end, we propose a dedicated approach named PALMR, by utilizing and extending the proximal alternating linearized minimization techniques. Theoretically, we investigate its convergence property, where it is shown to converge to a critical point under mild conditions. Empirically, we test our model on both synthetic and real diffusion tensor imaging data, and show that our model outperforms other multivariate regression models when manifold-valued responses contain gross errors, and is effective in identifying gross errors.

In this paper we formally analyse the use of sparse filtering algorithms to perform covariate shift adaptation. We provide a theoretical analysis of sparse filtering by evaluating the conditions required to perform covariate shift adaptation. We prove that sparse filtering can perform adaptation only if the conditional distribution of the labels has a structure explained by a cosine metric. To overcome this limitation, we propose a new algorithm, named periodic sparse filtering, and carry out the same theoretical analysis regarding covariate shift adaptation. We show that periodic sparse filtering can perform adaptation under the looser and more realistic requirement that the conditional distribution of the labels has a periodic structure, which may be satisfied, for instance, by user-dependent data sets. We experimentally validate our theoretical results on synthetic data. Moreover, we apply periodic sparse filtering to real-world data sets to demonstrate that this simple and computationally efficient algorithm is able to achieve competitive performances.

The news of Rahul Gandhi addressing technology experts in Silicon Valley of the US has set the explosion of Jokes on Rahul Gandhi in the Twitter world and various social media domains. The subject he had chosen to address is "ARTIFICAL INTELLIGENCE" which exactly matches the definition of Comedy as mentioned by Steve Martin "Comedy is the art of making people laugh without making them puke". Apparently, Rahul Gandhi will be accompanied by Chairman of Overseas Congress Department Sam Pitroda, who has reportedly fixed Rahul Gandhi's meeting with technology experts. He will also meet representatives of technological giants and startups working on the new science. The idea behind the visit, was for Gandhi to lead the domestic debate on the impending transition from software to artificial intelligence. Inspite of Nehru/Gandhi family being elected from Amethi since 1980, Amethi cuts a sorry figure when it comes to development and welfare of the people.

Duckietown is a hands-on, project-based course at MIT that focuses on self-driving vehicles and high-level autonomy. In Spring 2016, Liam Paull served as Duckietown's CEO and Teddy Ort worked as a vehicle autonomy engineer in training. Since the course began at MIT, it has spread to other universities around the globe, and is now taught in universities from Beijing to Zurich.

Voice assistants, including Apple's Siri and Amazon's Alexa, can be controlled by hackers using inaudible voice commands, researchers at Zhejiang University in China have found. This can be done using a technique that translates voice commands into ultrasonic frequencies that are too high for the human ear to recognise. The technique, named DolphinAttack, could be used to download a virus, send fake messages and even add fake events to a calendar. It could also give hackers access to outgoing video or phone calls, allowing them to spy on their victims. The fault is due to vulnerabilities in the software and hardware of speech recognition systems.

THE human face is a remarkable piece of work. The astonishing variety of facial features helps people recognise each other and is crucial to the formation of complex societies. So is the face's ability to send emotional signals, whether through an involuntary blush or the artifice of a false smile. People spend much of their waking lives, in the office and the courtroom as well as the bar and the bedroom, reading faces, for signs of attraction, hostility, trust and deceit. They also spend plenty of time trying to dissimulate.