The Internet can indeed alter specific brain regions and also affect our attention capacity, memory processes as well as social interaction. The Internet can indeed produce both acute and sustained alterations in specific areas of cognition.

Investigations have been on concerning leading hypotheses on how the Internet may be able to alter cognitive processes.

High Usage of Internet Can Impact Brain Functions

Further examination was carried out to see the extent to which these hypotheses were indeed supported by recent findings from psychological, psychiatric and neuroimaging research. The report did combine the evidence to produce revised models on how the Internet could indeed affect the brain’s structure, function, and cognitive development.

The key findings of this report are that high levels of Internet use could indeed impact on many functions of the brain. For example, the limitless stream of prompts, as well as notifications from the Internet, does encourage us towards constantly holding divided attention — which then, in turn, may indeed decrease our capacity for maintaining concentration on a single task.

Additionally, the online world now does present one with a uniquely large and constantly-accessible resource for facts and information, which is never more than a few taps and swipes away. Now, one has the most of the world’s factual information literally at one’s fingertips that it appears to have the potential to begin changing the ways in which it is stored.

The recent introduction as well as the widespread adoption of these online technologies, along with social media, is also of concern to some teachers as well as parents. The potential negative effects could be as simple as ensuring that children are not really missing out on other crucial developmental activities, such as social interaction and exercise, by spending too much time on digital devices. In order, to help with this, there are also now a multitude of apps as well as software programs available for restricting Internet usage and access on smartphones as well as computers.

There is much more to learn about the impact of the digital world on mental health as well as brain health.

• Taking a sauna session does cause the same effects on one’s body as a short, moderate workout.
• Complementary Therapies in Medicine do help such as a sauna and on a bicycle ergometer on separate days. It was felt that the heat dilates the blood vessels and that this lowers the blood pressure.
• People with low blood pressure or cardiovascular disorders have indeed traditionally been advised not to use saunas, as a further fall in blood pressure can rather lead to fainting.
• Although many previous assumptions have been made about the acute effects of sauna use, not much research has been done so far.
• The scientists conducted their study by recruiting 19 volunteers to measure the immediate effects of sauna use on the cardiovascular system.
• Indeed a sauna session is a physical strain and its long-term positive effects are similar to sports activities.
• No doubt, healthy sweating does not contribute to weight loss.

Home Internet News Amazon happens to be the most trusted among Internet Brands in India according to TRA Research.

Amazon is indeed the most trusted among Internet Brands in India says TRA Research

Among the 32 Internet brands that were actually surveyed, Hike ranked fourth, beating WhatsApp, which ranked 10th.

Highlights of Internet brands

• Google and Facebook do follow Amazon in order to take the top positions
• WhatsApp ranked 10th, while Hike surprisingly rather took the fourth spot
• Oyo Rooms happened to be in the fifth position on the ‘Brand Trust Report 2019’
• E-commerce major Amazon is considered to be the most trusted among the Internet brands in India, followed by Google as well as Facebook which is based upon a recent report by TRA Research.
• Among the 32 Internet brands that happened to be surveyed, Indian messaging app Hike ranked fourth, beating Facebook-owned Whatsapp, which ranked 10th, according to brand intelligence and data insights company’s “Brand Trust Report 2019”.
• Indian accommodation service provider as well as online hotel room aggregator Oyo Rooms was at the fifth position. The Indian online taxi aggregator Ola actually ranked sixth, much way ahead of US-based Uber, which ranked 14th.
• Online payment service provider Paytm ranked 19th, happen to be one place above its global peer PayPal.
• It has been felt by experts that many Indian Internet start-ups are leaders in their categories, which is no mean a feat. The brands that do focus on creating trust along with profits and growth are the ones that will survive in this business.

The other players in the top 10 were Flipkart, LinkedIn, and Snapdeal, in the seventh, eighth and ninth ranks, respectively.

• Xbox Game Pass ultimately was announced by Microsoft with Access to Xbox Game Pass for PC, Xbox Live Gold
• Redmi K20 Pro vs Redmi K20 Prices, Specifications need to be compared as well.
• Both the phones indeed have a lot of similarities, and the Redmi K20 and Redmi K20 Pro sport a triple rear camera setup and do have a mechanized pop-up selfie camera setup.
• It may be noted here that the Best Phone under 15000: The Best Mobiles under Rs. 15,000, June 2019 Edition
• Best phone under Rs 15,000 in India does feature Realme 3 Pro, Redmi Note 7 Pro, Samsung Galaxy M30, Oppo K1, as well as more. Here are the top picks for best mobiles under 15,000 in June 2019.
• The freshly-launched Redmi K20 goes head-to-head against the Realme X. Here’s a detailed comparison between the two phones.
• It is but obvious that Amazon is a very trusted brand and there are others also in the market. One can rely on Amazon for excellent service and it is in much demand no doubt.
• The other top players in the rank of 10 happen to be Flipkart, LinkedIn, and Snapdeal, in the seventh, eighth and ninth rank, respectively. The brands that do focus on creating trust along with profits and growth are the ones that will rather survive in this business.

One spends a lot of time on the internet. Youngsters and even adults search for information and also relax on the internet. There are several videos to be seen, but the amazing thing is that there are videos on the internet which make one sleep well.

Sleep cannot be done with and it is better to be entertained in order to have a good night’s sleep. One must use the internet to maximum advantage.

6 things on the Internet that will help you fight sleeplessness

1.Insomnia

Insomnia or sleeplessness happens to be a disorder that is characterized by the inability to fall asleep or stay asleep for the desired amount of time. Insomnia can indeed occur at any age but it is also more common in elderly people.

Most of the population suffers from insomnia and other sleep disturbances. One is quite aware of insomniacs who have indeed tried hard everything from eye masks to yoga asanas and chamomile tea to sleep better. One would like to sleep like a baby and try out many things in order to sleep well.

2. Food being garnished

Videos are shown of food being prepared that can make one hungry and may not seem relaxing to watch. But videos such as the sprinkling of cheese, melting chocolate and desserts that are being topped with icing sugar are truly relaxing. Video of icing the cake are especially relaxing. One needs to have a look at these videos indeed and thus improve one’s sleep pattern.

3. Short makeup tutorials

One is able to fall asleep only when one is not stressed out. There are in fact billions of quick beauty videos on the internet. And no doubt watching these calming and beautiful videos can help one to sleep well. Every individual after a tiring day wants to sleep well.

4. The boring history chapters

Sit down and also recall which history chapters or essays made one sleepy in the class. Did one not feel someone is pulling one’s eyes to shut them down? Open one such chapter or essay on one’s phone and start reading it. Enjoy the sleep that comes with it.

5 Calming rain music

The right ambiance will indeed make one feel relaxed and eventually make one fall off to sleep. Listen to some relaxing music on the internet before one sleeps tonight. Or one can just play the music on low volume and close one’s eyes; one will even realize when one is fast asleep.

6 Listen to podcasts and stories

If one follows any particular podcast every day, start listening to it at night before one sleep. One can also listen to audio stories, which can have a calming effect on one’s mind.

Sleep is essential for everybody. A good night sleep refreshes one and the next day one is able to perform their chores well. One feels refreshed the next day and very energetic. Even youngsters due to stress face sleep problems and adults do face such problems too.

June means showering one’s dad with gifts, as it happens it is father’s day. And if one’s dad is a gadget freak one does feel that he needs to up his gadget game. One can think of buying one of the best gadgets one can buy for one’s dad on this special day.

Father’s Day Gifts Ideas 2019

Digital camera

While one’s dad may have a phone to click pictures, it would be nice to gift him a digital camera and also allow him to take his interest in clicking pictures to the next level.

Temperature control lunch box

If one’s dad goes to work and carries a dabba that he has to heat once he is ready to have it at work, one can gift him a temperature control lunch box so he can eat warm food, simply by plugging in the wire. Stay rest assured, he will indeed boast about his new father’s day gift to his colleagues.

Beverage chiller

If one’s dad loves a chilled beer or a chilled nimbu pani, try gifting him a beverage chiller which is now readily available.

Fitness tracker wrist watches

Wishing for our dads to have a long and healthy life one needs to gift him on Father’s day. It is time to do something towards it. Gift him a fitness tracker that will rather encourage him to walk more and also allow him to monitor his heartbeat.

Home Assistant

Gift him Google Home/ Amazon Alexa and sync everything from the TV, playlist, phone to the lights of one’s home to give him added comfort. Watch him give away instructions to the home assistant and also have a smoother life.

Electronic frame

When one will gift one’s dad an electronic frame, one is giving him a lifetime of memories. Save pictures from his childhood to adulthood and let him also sit back and enjoy the display of pictures. Order one that has a fine LED screen and thus allows him to add as many pictures he wants

Kodak Printomatic Digital Instant Print Camera

Kodak has indeed created a new camera that prints images automatically after one pushes the shutter, making use of Zink photo paper, which is rather resistant to smudges, tearing and water damage.

This pocket-sized wonder of a gadget boasts 36 capabilities, including a measuring tape, flashlight, bottle opener, corkscrew and screwdriver with 26 different bits.

Retro Video Game Phone Case

Making use of interviews with specialists, online reviews and personal experience, present-day editors, writers, and experts do take care to recommend items that we really like and hope one will enjoy.

Kelvin Tools Urban Super-Tool

This pocket-sized wonder of a gadget does boast 36 capabilities, including a measuring tape, flashlight, bottle opener, corkscrew and screwdriver with 26 different bits.

Ember Temperature Control Ceramic Mug,

Get rid of lukewarm coffee or scalding hot tea warmed up in the microwave. This ceramic mug does allow him to control the temperature of his warm beverage with its accompanying app, which also suggests the perfect temperature for dozens of popular beverages.

The Foldaway Bicycle Helmet

One can present a collapsible helmet. It doe easily folds in half allowing for easy storage.

PhoneSoap Smartphone Sanitizer

Smartphones are indeed a breeding ground for bacteria as well as viruses. Simply place Dad’s phone inside, attach the charging cable and close the lid. Inside, ultra-violet lights do disinfect it. The case even does have built-in acoustic amplifiers that can indeed play music or the phone’s alarm while it’s being cleaned.

Nest Learning Thermostat,

One really wants complete control over the temperature at all times, the Nest Thermostat will allow Dad to change the air conditioning and heating settings from his smartphone or even making use of Alexa.

One often makes use of terms such as Data Science and machine learning interchangeably. However, while there is indeed an overlap between the two, they are distinct from each other in terms of roles as well as responsibilities.

Data Science happens to be a field that has been around for a while now. Machine learning is indeed a fairly new discipline and has now become even more about building algorithms as well as self-learning solutions. Even as the boundaries between both of them do continue to blur, the disciplines stand discrete in their respective own rights.

The Pillars of Data Science

One of the primary characteristics of Data Science is that it happens to be a multi-disciplinary study, and heavily utilizes scientific methodologies. More often than not, Data Science does exist at the junction of statistics, business knowledge as well as technical skills.

Data Science, at its base, is indeed a way to extract important information from structured and unstructured data. Data Science also does focus heavily on being able to derive informed decisions and also strategic moves from data often termed as ‘insights’.

This does make statistics one of the biggest parts of data science, as it in fact stands as a fundamental part of the approach. When trying to make sense of data, statistics is an invaluable tool as it does wrangle the data in an approachable manner.

Another one of the core components of data science happens to be business acumen, as, without this, meaningful as well as usable insights cannot be derived. The individual wrangling of the data and trying to extract knowledge from it indeed lead to awareness of the workings of the company.

As mentioned previously, insights are indeed important in a corporate setting. They can also enable the creation of new business strategies as well as avenues for development. They can also rather identify potential revenue leakages, pain points, and non-profitable ventures, as well as rather provide a more comprehensive view of the company’s operations.

Statistics alone is not sufficient to derive insights from the deluge of data that most companies handle today. This is where training models and algorithms come in.

The Roots of Machine Learning

Machine Learning happens to be an integral part of any data scientist’s approach to a problem. The rise of accessible machine learning has no doubt made it an ever-present part of data science.

At its base, machine learning is the process of writing an algorithm that can indeed learn as it consumes more data. ML has driven the importance of having a data scientist in every sort of big company. Owing to a large amount of data that data scientists have to handle, algorithms powered by ML are extremely important.

As of now, ML algorithms are indeed able to move the needle from descriptive and reactive business strategies to prescriptive as well as proactive business strategies. Moreover, this does represent a move from insights derived from collected data to predictions and projections derived from past patterns.

Machine Learning does allow data scientists to take on their roles to the next level, and also offers a novel way of management. Nowadays, an understanding of machine learning is integral to be a data scientist.

Data Science is more than ML

Data Science is indeed now becoming one of the more important parts of the functioning of an organization. An important distinction that has to be in fact made towards understanding the difference between this as well ML is that data science is a generalist approach while ML is a specialist approach.

Data Scientists

One does heavily benefit from a broad subject matter expertise area. This is owing to the varied nature of their role, as they will also be required to communicate the insights and their benefits to a non-technical audience. Even as they are generalists, data scientists do differ from organization to organization, as the needs of every company are different.

On the other hand, ML engineers are mainly tasked with creating tools that are made use of by data scientists. This does include cutting-edge models and efficient algorithms for use by data scientists. This is where one of the core differences between the designations that come in.

While it is indeed possible to directly scale machine learning capabilities by hiring more individuals, it is not possible to do so with data scientists. Hiring a data scientist does also include a period of learning and training, where the employee is required to know about the company’s processes.

Data Science operations cannot be scaled up directly, as there will be diminishing returns with a team of data scientists. The designation is also not really extensible to other companies, owing to the differences between business practices.

Therefore, it is important to make a distinction between data science as well as machine learning.

By 2023, seventy-five percent of large organizations indeed would hire artificial intelligence (AI) behavior forensic, privacy, and customer trust specialists in order to reduce brand and reputation risk.

Bias based on race, gender, age or even location, and bias based on a specific structure of data, has been indeed long-standing risks in training AI models.

75% firms to hire AI behavior forensic experts by 2023

New tools and skills are indeed required in order to help organizations identify these and other potential sources of bias, build more trust in using AI models, and also to reduce corporate brand as well as reputation risk. More and more data, as well as for analytics leaders and chief data officers (CDOs), are indeed hiring ML (machine learning) forensic and ethics investigators.

While the number of organizations that have been hiring ML forensic and ethics investigators does remain small today, that number will accelerate in the next five years. This is indeed much talked about turf of Artificial Intelligence that now even handles the preliminary part of ‘action’ that was needed in response to a ‘trigger’.

By 2023, seventy-five percent of large organizations would no doubt hire artificial intelligence (AI) behavior forensic, privacy as well as customer trust specialists in order to reduce brand as well as reputation risk. Bias based on race, gender, age or locations, and bias based on a specific structure of data, have indeed been long-standing risks in training AI models.

Newer forms of tools, as well as skills, are needed in order to help organizations to identify these and other potential sources of bias, build more trust in using AI models, and also reduce corporate brand as well as reputation risk. More and more data, as well as for analytics leaders and chief data officers (CDOs), are indeed hiring ML (machine learning) forensic as well as ethics investigators.

Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society. VOA Increasingly, sectors such as finance as well as technology are deploying combinations of AI governance and risk management tools along with techniques in order to manage reputation and security risks.

In addition, organizations such as Facebook, Google, Bank of America, MassMutual and NASA are hiring, or have already appointed, AI behavior forensic specialists who primarily do focus on uncovering undesired bias in AI models before they are deployed. While the number of organizations hiring ML forensic and ethics investigators does remain small today, that number will accelerate in the next five years no doubt.

In order to help manufacturing businesses, as well as healthcare, incorporate Artificial Intelligence (AI) into their work, software giant Microsoft has indeed decided to establish a training hub in Louisville, US. The company does plan to hire four “fellows” per year from the Louisville community who will indeed learn AI skills through the company’s proprietary curriculum. The initiative would also focus on other technologies such as the Internet of Things (IoT) and data science as well.

Machine learning has rather been defined by Andrew Ng, a computer scientist at Stanford University, as being “the science of getting computers to act without being explicitly programmed.” It was actually first conceived in the 1950s but experienced limited progress until around the turn of the 21st century. Since then, machine learning has been a driving force behind a number of innovations, most notably artificial intelligence.

Several different industries are indeed already benefiting from machine learning, and there is also an increasing demand for ML products as well as services across the developed world.

Businesses of all sorts are indeed taking advantage of its predictive capabilities, and also to seek to develop prescriptive machine learning methods in order to make informed decisions. There are many different ways for companies in order to approach this technology, including several programming languages that do stand out in the field accessible to the general public.

Python happens to be a dynamically typed language, which can also create problems in machine learning environments. For one, errors can become difficult to track as the program does grow larger and more complex. This can also create costly drawbacks and also slow down performance.

Also developed in the early 1990s, R happens to be a part of the GNU project. It is also widely used in data analysis and is typically applied to common machine learning tasks such as regression, classification and decision tree formation. It is also a very popular programming language among statisticians.

R is also open source and is also widely renowned for being relatively easy to install, configure and make use of. It is platform agnostic and integrates well with other programming languages. Along with data analysis, R has particularly strong data visualization capability.

Despite rather being relatively easy to integrate with other tools, R has some unique quirks that can make it somewhat confusing to learn, such as its unconventional data structures and indexing (which starts at 1 instead of 0. It also is less popular than Python and thus also does not have as much documentation available for machine learning applications.

JavaScript

Developed in the mid-1990s as a tool in order to improve on web development practices, JavaScript has no doubt since become one of the most widely utilized languages in that field. It is a high-level and dynamically typed language that is flexible and multi-paradigm. Although its applications in machine learning have been rather limited, high profile projects such as Google’s Tensorflow.js are based on JavaScript.

One of the most promising features of JavaScript in the field of machine learning is that it does open up opportunities for web and front-end developers, who are largely already well acquainted with it and thus have an accessible point of entry into an otherwise somewhat obscure as well as difficult niche.

As it does exists now, however, the ecosystem for machine learning with JavaScript is still somewhat immature, so support for this type of development is indeed currently limited. It also does lack the range of functionality for data science that languages like R and Python already have built into them.

C++

Among today’s most common programming languages, C++ is of course probably the oldest. Developed at Bell Labs in the early 1980s, C++ emerged out of doctoral research that sought to extend the C language. Enabled with both low and high-level programming

C++ works especially well for resource-intensive applications, which is part of what makes it indeed great for machine learning. And as a statically typed language, it can also execute tasks with relatively high speed.

However, C++ does require a great deal of complex code in order to build new applications, which can be highly time-consuming and can cause a great amount of difficulty in maintenance. This can also make it very easy for beginners to create errors
However, C++ does require a great deal of complex code in order to build new applications, which can be highly time-consuming and can also cause a great amount of difficulty in maintenance. This can also make it very easy for beginners to create errors.

Java

Developed by Sun Microsystems in the mid-1990s, was originally built to be high-level and is an object-oriented programming language that does look and feel similar to C++. Along with being extremely popular, Java can indeed implement a wide variety of algorithms, which are quite very useful to the machine learning community.

Java is of course regarded as one of the most secure programming languages, largely due to its use of bytecode and sandboxes. Java does manage to harness much of the power of C++ while overcoming its security issues and overall complexity.
But with all of its improvements over C++, Java has a reputation for being slower than many other programming languages.

Additionally, as of 2019, Java has rather implemented commercial licensing for certain business applications, which can indeed be costly.

Conclusion

Of all the programming languages being applied to machine learning, Python remains the most popular. Nevertheless, languages such as JavaScript could indeed likely grow in popularity as the landscape changes over time. And although human programming will never indeed go extinct (or at least not any time in the near future), programming for machine learning which will likely become less focused on code in coming years, as machines are trained to code themselves.

Samsung Electronics Co. is no doubt the third-largest number of patents related to artificial intelligence (AI), coming much behind two US tech giants. This has been revealed in a report. Microsoft Corp. has indeed had the most, with 18,363 AI-related patents as of January, followed by IBM Corp. with 15,046 and Samsung with 11,243. This is based upon a report let out by Germany-based researcher IPlytics.

Samsung has third-largest number of patents on AI

Five of the top 10 companies were no doubt based in the United States, two in Japan, one in South Korea, one in the Netherlands and one Germany, according to Yonhap news agency reported. The number of AI-related patents per year has indeed increased from 22,913 in 2008 to 78,085 in 2018 as more tech companies have delved into the replication of human intelligence by machines for use in a wide range of industries, it noted. The more countries in which an invention has been rather patented, the higher the perceived international market potential for the patented invention says a study. Samsung also happens to be the world’s largest smartphone maker, and has been racing to propel innovation in AI technology in order to foster new growth drivers, have been working on various types of AI applications in smartphones and other electronics in its AI labs.

Samsung Electronics Co. has indeed the third-largest number of patents related to artificial intelligence (AI), coming behind two U.S. tech giants, a report showed Friday. Microsoft Corp. has, in fact, the most, with 18,363 AI-related patents as of January, followed by IBM Corp. with 15,046 and Samsung with 11,243, according to the report by Germany-based researcher IPlytics.

Five of the top 10 companies are based in the United States, two in Japan, one in South Korea, one in the Netherlands and one Germany. The number of AI-related patents per year has jumped from 22,913 in 2008 to 78,085 in 2018 as indeed more tech companies have delved into the replication of human intelligence by machines for use in a wide range of industries, it noted.

The more countries in which an invention has been patented, the higher the perceived international market potential for the patented invention, the study claims. Samsung, also the world’s largest smartphone maker, has been racing to propel innovation in AI technology to foster new growth drivers, have been working on various types of AI applications in smartphones and other electronics in its AI labs.

Samsung is indeed a very popular smartphone and has made good outreach in the smartphone market. In the electronics industry, the Samsung smartphone has carved a well-established niche for itself and has competed well with other smartphones in the market.

In the field of Artificial Intelligence also it has proved well and has a patented invention it is much in demand. A product gains credibility by its authenticity and Samsung has achieved these parameters.

The industry has well received the Samsung smartphone and its usage is indeed gaining much ground in the smartphone market industry.

There is much future for machine learning. One uses a black marker to dot and diagram the nodes of the human brain: the parts that are “ruminative, that think deeply, that ponder.

A startup is trying to approximate these neurons and synapses in its next-generation computer processors, which the company is betting can “mechanize intelligence.”

Artificial intelligence is indeed often thought of as complex software that mines vast datasets, but Knowles and his co-founder, chief executive officer Nigel Toon, do argue that more important obstacles still do exist in the computers that run the software.

The problem, that has been observed is that chips — known, depending on their function, as CPUs (central processing units) or GPUs (graphics processing units) — were not actually designed to “ponder” in any recognizable human way.

Whereas of course, human brains make use of intuition to simplify problems such as identifying an approaching friend, a computer might indeed try to analyze every pixel of that person’s face, comparing it to a database of billions of images before attempting to say hello. That precision, which made indeed made a lot of sense when computers were primarily calculators, is massively inefficient for AI, burning huge quantities of energy to process all the relevant data.

When Knowles and the no doubt more business-minded Toon founded Graphcore in 2016, they put “less precise” computing at the heart of their chips, which they call intelligence processing units or IPUs.

The concepts in one’s brain are quite vague. It is really the aggregation of very approximate data points that do cause one to have precise thoughts. There are various theories on why human intelligence forms this way, but for machine learning systems, which do need to process huge and amorphous information structures known as “graphs,” building a chip that specializes in connecting node-like data points may prove key in the evolution of AI. Put another way, Graphcore is developing a brain for computers that, if its co-founders are right, will one be able to process information more like a human instead of faking it through massive feats of number crunching.

Simon Knowles, chief technology officer of Graphcore Ltd uses a black marker to dot and diagram the nodes of the human brain: the parts that are “ruminative, that think deeply, that ponder.” His startup is indeed trying to approximate these neurons and synapses in its next-generation computer processors, which the company is betting can “mechanize intelligence.”

Artificial intelligence is often regarded as complex software that mines vast datasets, but Knowles and his co-founder, Chief Executive Officer Nigel Toon, argue that more important obstacles still do exist in the computers that tend to run the software. The problem, they feel depends on their function, as CPUs (central processing units) or GPUs (graphics processing units)—were not designed to “ponder” in any recognizably human way.

Whereas human brains make use of intuition to simplify problems such as identifying an approaching friend, a computer might try to analyze every pixel of that person’s face, comparing it to a database of billions of images before rather attempting to say hello.