This ebook, based on the latest ZDNet / TechRepublic special feature, looks at the outlook for business leaders in 2020 and where they are spending their tech dollars. Gartner's outlook for the next five years revolves around the idea that artificial intelligence will augment human decisions, emotions and relationships. The big question is whether Gartner's prognostications will play out in the next five years. At the Gartner IT Symposium/Xpo, analyst Daryl Plummer outlined the trends to expect in the years ahead. By 2025, half of the people with a smartphone without a bank account will use a cryptocurrency account.

In recent years, machine learning and artificial intelligence have made a way into all areas of homes, lives and businesses. Today, the technology is becoming increasingly ubiquitous such as from AI assistants like Alexa or Siri, speech translator, GPS finding location, smart devices etc. These days, you can find exciting evidence of machine learning innovation in marketing today as it is applied to pay-per-click, created more intelligent email campaigns and chatbots. Here, GoodFirms has conducted a survey based on AI and Machine Learning for PCC Campaign Management. In this research, PPC Gurus (Duane Brown, Ed Leake, Gianluca Binelli, Jeff Sauer, Kirk Williams, Larry Kim, Luca Senatore, Martin Roettgerding, Navah Hopkins, Patrick Gilbert) were contacted that are experts in digital marketing.

Web pages and other contents such as local listings or videos are ranked or displayed on Search Engines such as Google, Bing and Yahoo search results, based on what the search engine considers to be most relevant to users. An SEO friendly website will automatically rank higher in search results. This has a great effect on traffic flow which is also very essential at will enhance internet visibility. Search engine optimization in Kenya is an important aspect of website development. Without quality search engine optimization services it is hard to achieve top search engine rankings.

Last March, Google took the wraps off of Coral, a collection of hardware development kits and accessories intended to bolster the development of machine learning models at the edge. It launched in select regions in beta, but the tech giant today announced that it's graduating to a "wider" and global release. All Coral products -- including the $150 Coral Dev Board, the $74.99 Coral USB Accelerator, and the $24.99 5-megapixel camera accessory -- are available for sale at electronics retailer Mouser and for large-volume sale through Google's sales team. The company says that by the end of the year, it'll expand distribution into new markets including Taiwan, Australia, New Zealand, India, Thailand, Singapore, Oman, Ghana, and the Philippines. Coinciding with Coral's general availability, the Coral website -- which now lives at Coral.ai -- has been revamped with better organization for docs and tools, testimonials, and "industry-focused" pages. Additionally, it links to a new set of examples aimed at providing solutions to common AI problems, such as image classification, object detection, pose estimation, and keyword spotting.

In this research guide, we'll look at deep learning papers aimed at synthesizing video frames within an existing video. This could be in between video frames, known as interpolation, or after them, known as extrapolation. The better part of this guide will cover interpolation. Interpolation is useful in software editing tools as well as in generating video animations. It can also be used to generate clear video frames in sections where a video is blurred. Video frame interpolation is a very common task, especially in film and video production. Optical flow is one of the common tactics used in solving this problem.

With the industry 4.0 revolution around, Research N Reports presents a detailed analysis of Cognitive Computing market that offers latest insights for business professionals. Using BI tools such as Factiva and Hoover, the report offers a comprehensive analysis and is a mix of market intelligence studies and industry insights. Prepared by a panel of highly experienced market analysts and consultants, the report is spread across 137 pages offering chapter wise detailed market analysis that enables the clients with multiple data points and encourages them to have a 360 degree overview of the market performance. Clients can ask for sample of this report that gives a detailed overview of the market conditions, driving and restraining factors, segments, trends and opportunities. Covering the latest information about the market, the samples can give a basic understanding upon the report contents and its format.

Arm aims to take machine learning to mainstream and low-end devices with the launch of its new neural processing units (NPUs). The company is unveiling the Ethos-N57 and Ethos-N37 NPUs, which it will license to chipmakers who can integrate it into their products. The idea is to extend the range of Arm machine learning (ML) processors to enable artificial intelligence (AI) applications in mainstream devices. The company also unveiled the Mali-G57 graphics processing unit (GPU). This is the first mainstream Valhall architecture-based GPU, delivering 1.3 times better performance over previous generations.

Noninvasive MR-guided focused ultrasound (MRgFUS) treatments are promising alternatives to the surgical removal of malignant tumors. A significant challenge is assessing the treated tissue immediately after MRgFUS procedures. Although current clinical assessment uses the immediate nonperfused volume (NPV) biomarker derived from contrast enhanced imaging, the use of contrast agent prevents continuing MRgFUS treatment if margins are not adequate. In addition, the NPV has been shown to provide variable accuracy for the true treatment outcome as evaluated by follow-up biomarkers. This work presents a novel, noncontrast, learned multiparametric MR biomarker that is conducive for intratreatment assessment. MRgFUS ablations were performed in a rabbit VX2 tumor model. Multiparametric MRI was obtained both during and immediately after the MRgFUS ablation, as well as during follow-up imaging. Segmentation of the NPV obtained during follow-up imaging was used to train a neural network on noncontrast multiparametric MR images. The NPV follow-up segmentation was registered to treatment-day images using a novel volume-conserving registration algorithm, allowing a voxel-wise correlation between imaging sessions. Contrasted with state-of-the-art registration algorithms that change the average volume by 16.8%, the presented volume-conserving registration algorithm changes the average volume by only 0.28%. After registration, the learned multiparametric MR biomarker predicted the follow-up NPV with an average DICE coefficient of 0.71, outperforming the DICE coefficient of 0.53 from the current standard of NPV obtained immediately after the ablation treatment. Noncontrast multiparametric MR imaging can provide a more accurate prediction of treated tissue immediately after treatment. Noncontrast assessment of MRgFUS procedures will potentially lead to more efficacious MRgFUS ablation treatments.

We provide a novel analysis of low rank tensor completion based on hypergraph expanders. As a proxy for rank, we minimize the max-quasinorm of the tensor, introduced by Ghadermarzy, Plan, and Yilmaz (2018), which generalizes the max-norm for matrices. Our analysis is deterministic and shows that the number of samples required to recover an order-$t$ tensor with at most $n$ entries per dimension is linear in $n$, under the assumption that the rank and order of the tensor are $O(1)$. As steps in our proof, we find an improved expander mixing lemma for a $t$-partite, $t$-uniform regular hypergraph model and prove several new properties about tensor max-quasinorm. To the best of our knowledge, this is the first deterministic analysis of tensor completion.

Deep Reinforcement Learning Based Power control for Wireless Multicast Systems Ramkumar Raghu 1, Pratheek Upadhyaya 1, Mahadesh Panju 1, V aneet Aggarwal 1,2, and Vinod Sharma 1 1 Indian Institute of Science, Bangalore, INDIA. Abstract -- We consider a multicast scheme recently proposed for a wireless downlink in [1]. It was shown earlier that power control can significantly improve its performance. However for this system, obtaining optimal power control is intractable because of a very large state space. Therefore in this paper we use deep reinforcement learning where we use function approximation of the Q-function via a deep neural network. We show that optimal power control can be learnt for reasonably large systems via this approach. The average power constraint is ensured via a Lagrange multiplier, which is also learnt. Finally, we demonstrate that a slight modification of the learning algorithm allows the optimal control to track the time varying system statistics. I NTRODUCTION Wireless networks are being constantly refined to cater for seamless delivery of huge amount of data to the end users. With increased user generated contents and proliferation of social networking sites, almost 78% of mobile data traffic is expected to be due to mobile videos [2]. Also, the requested traffic for these contents is ridden with redundant requests [3]. Thus, multicasting is a natural way to address these requests. A multicast queue with network coding is studied in [4], [5] with infinite library of files.