Google is one of the most used search engines online in comparison with the other search engines. I have a great respect for Sergey Brin and Larry Page. They have done some great innovative research programs especially in health sector, projects such as improving the future of healthcare by implementing structured data and AI. Google is one of my daily tools when working on projects. The reason of my frustration is that Google has been a biased search engine even after its A.I and machine learning implementation.

In the middle of the highway, my father turned to our car navigation system for directions. Before chalking out a route from Calcutta to Santiniketan, the system asked our language preference -- would we want to be guided through the alleys, bridges and villages in English, Bengali, Hindi, Marathi, Malayalam, the list went on and on. We have come a long way since the days when the only way to communicate with devices was via the keyboard or keypad. But how does a computer, or any system built on technology, think, understand, and, in the case of car navigation systems, even speak in regional languages? Artificial intelligence, of course -- one of the most important parts of which is the concept of natural language processing or NLP.

Earlier last year, Amazon released their machine learning chip AWS Inferentia at their AWS re:Invent conference. Designed by Amazon-owned Israeli company that works on building semiconductor systems, the inference chips are geared at larger workloads. In an earlier article, we pointed out how the release of inference chip is an indication of AWS's intention of strengthening its hardware muscle and also countering other cloud competitors like Azure and Google Cloud Platform. Now, Intel is fighting a battle for market size by emerging as a leader in AI compute performance and also winning over significant mindshare -- a turf that clearly belonged to NVIDIA so far.

Build stuff or buy stuff? China has long been a believer in the former to deal with a slowdown in its economy. Now it's trying to shift the emphasis to the latter. This year will be a big test of how far it's come in the transition from state-backed investment to domestic consumption as the main driver of growth. China's President Xi Jinping has warned of a "struggle" as his country faces an economic slowdown, the likes of which it hasn't seen for almost 30 years. A series of stimulus measures have been unveiled by the government not to boost the economy, but to manage the slowdown.

Since 2012, a sophisticated artificial intelligence has dug through big data to find signs of corruption in the Chinese government -- but local officials in many areas are now shutting it down, according to the South China Morning Post. One researcher involved in the program, which is portentously called "Zero Trust," told the Hong Kong newspaper that local officials might be shutting the program down because they don't "feel quite comfortable with the new technology." But the SCMP has another explanation: the AI works too well. It might notice a suspicious transfer of money, for instance, or a new car or property registered to the name of a government official's family or friends.

Alphabet, the parent company of the internet search giant Google, earned $39.27bn in the last three months of 2018, but its share price sank as its costs rose. Alphabet's revenues for the quarter were 22% higher than the same period last year and the company made a profit of $8.9bn, the company announced on Monday. Revenues in the US rose 20% while revenues from Europe, the Middle East and Africa rose 29%, helped by the strength of the euro and the pound. It was the latest tech company to announce strong revenue growth – news that has cheered investors – but rising costs that come as the company is facing increasing competition from Amazon clouded the news. The fees that Alphabet pays to companies like Apple for Google to be their default search engine rose to $7.4bn up from $6.6bn for the same period last year.

This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted. It is recommended that AI/XAI researchers be encouraged to include in their research reports fuller details on their empirical or experimental methods, in the fashion of experimental psychology research reports: details on Participants, Instructions, Procedures, Tasks, Dependent Variables (operational definitions of the measures and metrics), Independent Variables (conditions), and Control Conditions.

AI software is still software. Software engineers need better tools to make better use of AI software. For example, for software defect prediction and software text mining, the default tunings for software analytics tools can be improved with "hyperparameter optimization" tools that decide (e.g.,) how many trees are needed in a random forest. Hyperparameter optimization is unnecessarily slow when optimizers waste time exploring redundant options (i.e., pairs of tunings with indistinguishably different results). By ignoring redundant tunings, the Dodge(E) hyperparameter optimization tool can run orders of magnitude faster, yet still find better tunings than prior state-of-the-art algorithms (for software defect prediction and software text mining).

Neural Networks (NN) have recently emerged as backbone of several sensitive applications like automobile, medical image, security, etc. NNs inherently offer Partial Fault Tolerance (PFT) in their architecture; however, the biased PFT of NNs can lead to severe consequences in applications like cryptography and security critical scenarios. In this paper, we propose a revised implementation which enhances the PFT property of NN significantly with detailed mathematical analysis. We evaluated the performance of revised NN considering both software and FPGA implementation for a cryptographic primitive like AES SBox. The results show that the PFT of NNs can be significantly increased with the proposed methodology.

Today, many public or private institutions provide professional food service for personnels working in their own organizations. Regarding the planning of the said service, there are some obstacles due to the fact that the number of the personnel working in the institutions is generally high and the personnel are out of the institution due to personal or institutional reasons. Because of this, it is difficult to determine the daily food demand, and this causes cost, time and labor loss for the institutions. Statistical or heuristic methods are used to remove or at least minimize these losses. In this study, an artificial intelligence model was proposed, which estimates the daily food demand quantity using artificial neural networks for businesses. The data are obtained from a refectory database of a private institution with a capacity of 110 people serving daily meals and serving at different levels, covering the last two years (2016-2018). The model was created using the MATLAB package program. The performance of the model was determinde by the Regression values, the Mean Absolute Percentage Error (MAPE) and the Mean Squared Error (MSE). In the training of the ANN model, feed forward back propagation network architecture is used. The best model obtained as a result of the experiments is a multi-layer (8-10-10-1) structure with a training R ratio of 0,9948, a testing R ratio of 0,9830 and an error rate of 0,003783, respectively. Experimental results demonstrated that the model has low error rate, high performance and positive effect of using artificial neural networks for demand estimating.