Health and Fitness Wearable Marketresearch is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis.

Health and Fitness Wearable Market is growing at a High CAGR during the forecast period 2020-2026. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market.

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Top Companies of this Market includes: Athos, Ava, Catapult Sport, Fitbit, Garmin, Huami, Huawei, Jabra, Moov, Omron, Polar, Samsung, Sensoria, Whoop, Wyze, Xiaomi.

This report provides a detailed and analytical look at the various companies that are working to achieve a high market share in the global Health and Fitness Wearable market. Data is provided for the top and fastest growing segments. This report implements a balanced mix of primary and secondary research methodologies for analysis. Markets are categorized according to key criteria. To this end, the report includes a section dedicated to the company profile. This report will help you identify your needs, discover problem areas, discover better opportunities, and help all your organizations primary leadership processes. You can ensure the performance of your public relations efforts and monitor customer objections to stay one step ahead and limit losses.

The report provides insights on the following pointers:

Market Penetration:Comprehensive information on the product portfolios of the top players in the Health and Fitness Wearable market.

Product Development/Innovation:Detailed insights on the upcoming technologies, R&D activities, and product launches in the market.

Competitive Assessment: In-depth assessment of the market strategies, geographic and business segments of the leading players in the market.

Market Development:Comprehensive information about emerging markets. This report analyzes the market for various segments across geographies.

Market Diversification:Exhaustive information about new products, untapped geographies, recent developments, and investments in the Health and Fitness Wearable market.

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The cost analysis of the Global Health and Fitness Wearable Market has been performed while keeping in view manufacturing expenses, labor cost, and raw materials and their market concentration rate, suppliers, and price trend. Other factors such as Supply chain, downstream buyers, and sourcing strategy have been assessed to provide a complete and in-depth view of the market. Buyers of the report will also be exposed to a study on market positioning with factors such as target client, brand strategy, and price strategy taken into consideration.

Key Influence of the Health and Fitness Wearable Market report:

Table of Contents

Global Health and Fitness Wearable Market Research Report 2020

Chapter 1 Health and Fitness Wearable Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Health and Fitness Wearable Market Forecast

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Future Prospects of Health and Fitness Wearable Market to Witness Robust Expansion by 2026 with Top Key Players like Athos, Ava, Catapult Sport,...

ORBIS MARKET REPORTS has recently announced Global Health and Fitness Software Market report with all the critical analysis on current state of industry, demand for product, environment for investment and existing competition. Global Health and Fitness Software Marketreport is a focused study on various market affecting factors and comprehensive survey of industry covering major aspects like product types, various applications, top regions, growth analysis, market potential, challenges for investor, opportunity assessments, major drivers and key players

This exhaustive research account on Global Health and Fitness Software Market recently compiled and published, features noteworthy highpoints about various key progresses across regions, inclusive of details on country-wise developments as well as competition spectrum, technological indicators as well as product and service diversification.

The report in the following sections, accentuates details on various market players, stakeholders, and participants. Details on upstream and downstream developments, production and consumption patterns are also addressed in the report to influence holistic and balanced growth in the Global Health and Fitness Software Market.

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Vendor Landscape Analysis: Global Health and Fitness Software Market

The Major Players Covered in Global Health and Fitness Software Market are: MINDBODY AcuityScheduling PerfectGymSolutions BookSteam FitSW Optimity TeamApp TiltSoftware WodRack GoMotive LuckyFit BioExSystems SportSoft TRIIB zingfit VINT

Global Health and Fitness Software Market by Type: Web-based App-based

Global Health and Fitness Software Market by Application: SmallBusiness MiddleBusiness LargeBusiness

Market segment by Regions/Countries, this report covers United States Europe China Japan Southeast Asia India Central & South America

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Region-specific Segmentation: Global Health and Fitness Software Market

Details about most growth favorable regions have also been touched upon in the report besides additional and exclusive details on country-specific events that collectively influence unhindered growth in Global Health and Fitness Software Market.

Global Health and Fitness Software Market: Highlights

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Excerpt from:
Health and Fitness Software Market Is Expected To Reach Multimillion Usd By The End Of 2025: MINDBODY AcuityScheduling PerfectGymSolutions BookSteam...

A Leeds health and fitness therapist has seen a 50% increase in its number of clients during the Covid-19 pandemic.

Fitness Fusion has not only seen an increase in clients from Yorkshire but has also treated people across the UK via Zoom.

The company said that uncertainty, redundancies, fear of becoming ill, and the grief of losing loved ones have caused an increase in mental health issues during the pandemic, with that number predicted to increase if lockdowns are reimposed.

Anna Ferguson from Fitness Fusion uses a technique known as brain working recursive theory (BWRT), a method of psychotherapy which is rooted in neuroscience, with the aim of helping people to reduce binge eating triggered by the anxiety of the pandemic.

Anna commented: With all the uncertainty around coronavirus and the impact its having on our day to day lives, Im seeing an increase in the number of people coming to me because their emotional eating monster has taken over their lives and is making them miserable.

BWRT was created by UK author and psychotherapist Terence Watts, who said: The therapy works on the hindbrain that controls the fight or flight response we experience when exposed to danger.

If it recognises a pattern that triggers anxiety, it automatically prompts the release of hormones such as adrenaline and cortisol, which can trigger our need to snack, amongst other things.

Looking to promote your product/service to SME businesses in your region? Find out how Bdaily can help

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Leeds health and fitness therapist sees 50% client increase - Bdaily

Caregiving does not cause depression. However, caregivers often overlook their own self-care because they are diligently trying to provide the best possible care for their loved ones.

Because depression is often seen as a sign of weakness, or because of embarrassment, caregivers might hide their feelings from others and continue to ignore the condition, and their mental health will suffer. Untreated depression can inhibit the caregivers ability to provide care to his or her loved one and affect physical health as well.

Caregivers should learn to recognize the signs of depression, such as feelings of sadness, hopelessness or helplessness; difficulty sleeping; feeling exhausted, overburdened or overwhelmingly stressed all the time.

If you have these feelings, that could be an indication you are suffering from some depression.

Studies have shown that caregivers, regardless of age, suffer from excess depression, medical illness and even mortality compared with noncaregivers.

Depression is typically diagnosed when signs and symptoms are consistently occurring throughout the day, every day, for an extensive period.

Individuals experience depression in various ways that can change over time. Pay attention to symptoms such as: fatigue and loss of energy; weight loss or weight gain; changes in appetite; anxiety and irritation; trouble concentrating; memory impairment; feelings of worthiness; guilt and self-blame; unexplained physical ailments; and even suicidal thoughts.

Oftentimes, caregivers set unrealistic expectations on themselves and their caregiving roles, and they lean toward perfection or compare themselves to other caregivers. These expectations can cause undue physical and emotional stress and strain, leading to depression.

Caregivers can reduce the risk of depression by enlisting the support of family and friends, participating in support groups or by using respite services that can give them a break.

Additionally, practicing self-care, such as implementing a routine of physical exercise, a healthy diet and keeping a regular sleep schedule, can be effective strategies in warding off the symptoms of depression.

Activities, including meditation, yoga and creative expression, and taking time out to enjoy community or cultural events, as well as socializing with friends can also reduce the risk of depression.

Further, being more informed and finding resources about Alzheimers disease will help the caregiver with tips and strategies for caring for his or her loved one, which can reduce the stresses of caregiving that leads to depression.

For the caregivers health and the health of his or her loved one, its important for the caregiver to see a doctor to get a proper diagnosis. Whether the caregiver is assessed by a primary care provider, a psychiatrist, psychologist or other mental health professional, an accurate diagnosis can open the door to receiving treatment and counseling and put you on the road to a healthy recovery and a better sense of well-being.

Questions about Alzheimer's disease or related disorders can be sent to Dana Territo, the Memory Whisperer, owner of Dana Territo Consulting, LLC, at thememorywhisperer@gmail.com.

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Alzheimers Q&A: How can you identify depression in caregivers? - The Advocate

News

You can vote now for the winner of the 2020 Health Hero challenge.

You can start voting right now for the winner of the 2020 Be Well Philly Health Hero Challenge.

Were thrilled to announce that we now have our three finalists for the 2020 Be Well Philly Health Hero Challenge presented by Independence Blue Cross. Here at Be Well Philly, we constantly strive to highlight people who are helping others in the greater Philadelphia area live healthier and better lives. The Health Hero Challenge is our way of honoring the incredible and often unseen work that people do every day, even in the most challenging of circumstances.

We set out at the beginning of this challenge looking for medical providers, nonprofit leaders, entrepreneurs, teachers, anyone really whos making a difference in our community from a health and wellness perspective. And, you all delivered. You shared powerful personal stories of the heroes making a difference in our community, which led to to the nomination of these three finalists. Now, its time to choose the winner.

Name: Asasiya Muhammad (@thepeoplesmidwife), womens health advocate and midwife at Inner Circle Midwifery (@innercirclephilly), a private home birth practice based in Germantown.

Nonprofit of choice: Maternity Care Coalition. Since 1980, Maternity Care Coalition has assisted more than 135,000 families throughout Southeastern Pennsylvania, focusing particularly on neighborhoods with high rates of poverty, infant mortality, health disparities, and changing immigration patterns. A familys needs change as they go through pregnancy and their childs first years, and MCC offers a range of services and programs for every step along the way.

What motivates you to try to make Philadelphia a healthier place, and what policy would you institute if you could? I cherish Philadelphia as the place where I have had my most life-shaping experiences, some high such as graduating college, becoming a mother, and raising a family and others low. Ive had experiences that have left me feeling isolated, lost, and voiceless. As a Black mother, I have faced the feelings of fear and uncertainty many Black women in Philadelphia face, because of the haunting statistic that we are two times more likely to die during pregnancy, or within a year of giving birth due to pregnancy-related complications. As a midwife, I understand that the majority of these complications are preventable, and therefore have made it my mission to build a community-based a practice that is committed to diminishing this disparity in Philadelphia. My practice is unique in that it has a wraparound care component that bundles nutritional counseling and fitness classes into standard midwifery care.

I would institute a policy that expanded the use of and access to community-based providers and particularly related to those specializing in natural health and food access. This would look like expanding insurance access to providers like midwives, doulas, nutritional counselors, and fitness experts so that these services are both normalized and accessible to a larger part of the population. This would further look like creating sustainable food cooperatives in neighborhoods that are distant from larger markets and who now rely on stores that mostly carry processed foods.

Name: Vicky Borgia, a local doctor who utilizes Direct Primary Care (DPC), an alternative payment model for healthcare services. She specializes in reproductive health, LGBTQIA health and integrative medicine.

Nonprofit of choice: Womens Medical Fund. Racial justice and reproductive justice issues are intertwined. In 1976, Congress banned federal Medicaid coverage for abortion through the Hyde amendment. Then, in 1985, Pennsylvania prohibited state Medicaid coverage for abortion. Since then, Womens Medical Fund has provided funding to thousands of individuals struggling to get by and enrolled in Medicaid. Womens Medical Fund has expanded their mission to include advocacy and community organizing.

What motivates you to try and make Philadelphia a healthier place, and what policies would you institute if you could? I believe that healthcare is a human right. I serve communities that have historically been medically disenfranchised because I can use my skills and education to make changes in a system rife with health inequities. I center access, inclusion and equity in my direct primary care practice, which enables me to take the time I need with my patients and give the care they deserve.

Since it is well-established that racism and other forms of systemic oppression are major factors in increased morbidity, mortality, and generational trauma for all, my policy recommendations focus on dismantling systems of oppression in Philadelphia. This includes reprioritizing city investments from policing and instituting PILOTS where big health and educational nonprofits would volunteer a portion of their revenue to the general fund. These resources could be then be used to fund education and invest in communities by improving access to services, opportunities, food, and healthcare.

Name: Christy Silva, cofounder of Aidans Heart Foundation, a nonprofit committed to providing awareness, education, and support to the communities of the southeast Pennsylvania region and its surrounding area to create heart-safe communities for youth regarding the prevention of, or response to, tragic instances of Sudden Cardiac Arrest.

What motivates you to try to make Philadelphia a healthier place and what policy would you institute if you could ?

My motivation for wanting to make Greater Philadelphia a healthier place actually comes from a tragedy in my family. In September of 2010, my seven-year-old son Aidan, who had no prior health conditions, died without warning from sudden cardiac arrest, or SCA. I had never even heard of SCA prior to his death. As I struggled with my grief and tried to understand why my seemingly healthy child collapsed one sunny Saturday, I plunged into research. I learned that, nationally, approximately one out of every 300 youth has an undetected heart condition that could cause SCA. The American Heart Association quotes that more than 7,000 children under age 18 are struck by SCA each year. This equates to one young person, nearly every hour, every day, every year. Its a little known fact that Sudden Cardiac Arrest (SCA) is the leading cause of death in student athletes on school grounds. As a result of what I learned, I became determined to still be Aidans mom and try to prevent SCA from taking more young lives in our local communities. Im motivated by these facts to do everything possible to decrease the number of preventable deaths in young people in Philadelphia and its surrounding suburbs. I co-founded Aidans Heart Foundation shortly after Aidans death. To date, we have placed 89 AEDs in youth based sports facilities, trained 6,100 youth on how to perform CPR and how to use an AED, and we have partnered with pediatric cardiologists to provide 2,100 free heart screenings to kids and teens in efforts to detect heart conditions through a simple, non-invasive ECG exam.

If I could institute a policy to make Greater Philadelphia a healthier region, it would center around protecting hearts. Annual electrocardiogram exams at every well-child visit, particularly for young athletes; CPR and AED training for all teachers, coaches, instructors, etc. who work with physically active youth; and AED devices available in all schools, child care centers, dance, martial arts, gymnastics and other studios where kids are active, in addition to AEDs being prominently placed on every athletic playing field. These arent impossible tasks, but they do take the awareness of the public, particularly parents, to urge our community leaders and policymakers to implement these measures. We owe it to our kids to keep them safe at play.

Vote now to select your 2020 winner. Remember: the winner will be named the 2020 Health Hero and will receive a $15,000 donation to her charity of choice, and the two runners-up will each receive $2,500 donations to the charities of their choice.

Vote HERE now. (Remember, you can vote once a day until November 16th!) Stay in touch with @bewellphilly and @phillymagevents.

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Meet This Year's Be Well Philly Health Hero Finalists - Philadelphia magazine

Reinforcement learning is a subset of machine learning. It enables an agent to learn through the consequences of actions in a specific environment. It can be used to teach a robot new tricks, for example.

Reinforcement learning is a behavioral learning model where the algorithm provides data analysis feedback, directing the user to the best result.

It differs from other forms of supervised learning because the sample data set does not train the machine. Instead, it learns by trial and error. Therefore, a series of right decisions would strengthen the method as it better solves the problem.

Reinforced learning is similar to what we humans have when we are children. We all went through the learning reinforcement when you started crawling and tried to get up, you fell over and over, but your parents were there to lift you and teach you.

It is teaching based on experience, in which the machine must deal with what went wrong before and look for the right approach.

Although we dont describe the reward policy that is, the game rules we dont give the model any tips or advice on how to solve the game. It is up to the model to figure out how to execute the task to optimize the reward, beginning with random testing and sophisticated tactics.

By exploiting research power and multiple attempts, reinforcement learning is the most successful way to indicate computer imagination. Unlike humans, artificial intelligence will gain knowledge from thousands of side games. At the same time, a reinforcement learning algorithm runs on robust computer infrastructure.

An example of reinforced learning is the recommendation on Youtube, for example. After watching a video, the platform will show you similar titles that you believe you will like. However, suppose you start watching the recommendation and do not finish it. In that case, the machine understands that the recommendation would not be a good one and will try another approach next time.

[Read: What audience intelligence data tells us about the 2020 US presidential election]

Reinforcement learnings key challenge is to plan the simulation environment, which relies heavily on the task to be performed. When trained in Chess, Go, or Atari games, the simulation environment preparation is relatively easy. Building a model capable of driving an autonomous car is key to creating a realistic prototype before letting the car ride the street. The model must decide how to break or prevent a collision in a safe environment. Transferring the model from the training setting to the real world becomes problematic.

Scaling and modifying the agents neural network is another problem. There is no way to connect with the network except by incentives and penalties. This may lead to disastrous forgetfulness, where gaining new information causes some of the old knowledge to be removed from the network. In other words, we must keep learning in the agents memory.

Another difficulty is reaching a great location that is, the agent executes the mission as it is, but not in the ideal or required manner. A hopper jumping like a kangaroo instead of doing what is expected of him is a perfect example. Finally, some agents can maximize the prize without completing their mission.

Games

RL is so well known today because it is the conventional algorithm used to solve different games and sometimes achieve superhuman performance.

The most famous must be AlphaGo and AlphaGo Zero. AlphaGo, trained with countless human games, has achieved superhuman performance using the Monte Carlo tree value research and value network (MCTS) in its policy network. However, the researchers tried a purer approach to RL training it from scratch. The researchers left the new agent, AlphaGo Zero, to play alone and finally defeat AlphaGo 1000.

Personalized recommendations

The work of news recommendations has always faced several challenges, including the dynamics of rapidly changing news, users who tire easily, and the Click Rate that cannot reflect the user retention rate. Guanjie et al. applied RL to the news recommendation system in a document entitled DRN: A Deep Reinforcement Learning Framework for News Recommendation to tackle problems.

In practice, they built four categories of resources, namely: A) user resources, B) context resources such as environment state resources, C) user news resources, and D) news resources such as action resources. The four resources were inserted into the Deep Q-Network (DQN) to calculate the Q value. A news list was chosen to recommend based on the Q value, and the users click on the news was part of the reward the RL agent received.

The authors also employed other techniques to solve other challenging problems, including memory repetition, survival models, Dueling Bandit Gradient Descent, and so on.

Resource management in computer clusters

Designing algorithms to allocate limited resources to different tasks is challenging and requires human-generated heuristics.

The article Resource management with deep reinforcement learning explains how to use RL to automatically learn how to allocate and schedule computer resources for jobs on hold to minimize the average job (task) slowdown.

The state-space was formulated as the current resource allocation and the resource profile of jobs. For the action space, they used a trick to allow the agent to choose more than one action at each stage of time. The reward was the sum of (-1 / job duration) across all jobs in the system. Then they combined the REINFORCE algorithm and the baseline value to calculate the policy gradients and find the best policy parameters that provide the probability distribution of the actions to minimize the objective.

Traffic light control

In the article Multi-agent system based on reinforcement learning to control network traffic signals, the researchers tried to design a traffic light controller to solve the congestion problem. Tested only in a simulated environment, their methods showed results superior to traditional methods and shed light on multi-agent RLs possible uses in traffic systems design.

Five agents were placed in the five intersections traffic network, with an RL agent at the central intersection to control traffic signaling. The state was defined as an eight-dimensional vector, with each element representing the relative traffic flow of each lane. Eight options were available to the agent, each representing a combination of phases, and the reward function was defined as a reduction in delay compared to the previous step. The authors used DQN to learn the Q value of {state, action} pairs.

Robotics

There is an incredible job in the application of RL in robotics. We recommend reading this paper with the result of RL research in robotics. In this other work, the researchers trained a robot to learn policies to map raw video images to the robots actions. The RGB images were fed into a CNN, and the outputs were the engine torques. The RL component was policy research guided to generate training data from its state distribution.

Web systems configuration

There are more than 100 configurable parameters in a Web System, and the process of adjusting the parameters requires a qualified operator and several tracking and error tests.

The article A learning approach by reinforcing the self-configuration of the online Web system showed the first attempt in the domain on how to autonomously reconfigure parameters in multi-layered web systems in dynamic VM-based environments.

The reconfiguration process can be formulated as a finite MDP. The state-space was the system configuration; the action space was {increase, decrease, maintain} for each parameter. The reward was defined as the difference between the intended response time and the measured response time. The authors used the Q-learning algorithm to perform the task.

Although the authors used some other technique, such as policy initialization, to remedy the large state space and the computational complexity of the problem, instead of the potential combinations of RL and neural network, it is believed that the pioneering work prepared the way for future research in this area

Chemistry

RL can also be applied to optimize chemical reactions. Researchers have shown that their model has outdone a state-of-the-art algorithm and generalized to different underlying mechanisms in the article Optimizing chemical reactions with deep reinforcement learning.

Combined with LSTM to model the policy function, agent RL optimized the chemical reaction with the Markov decision process (MDP) characterized by {S, A, P, R}, where S was the set of experimental conditions ( such as temperature, pH, etc.), A was the set of all possible actions that can change the experimental conditions, P was the probability of transition from the current condition of the experiment to the next condition and R was the reward that is a function of the state.

The application is excellent for demonstrating how RL can reduce time and trial and error work in a relatively stable environment.

Auctions and advertising

Researchers at Alibaba Group published the article Real-time auctions with multi-agent reinforcement learning in display advertising. They stated that their cluster-based distributed multi-agent solution (DCMAB) has achieved promising results and, therefore, plans to test the Taobao platforms life.

Generally speaking, the Taobao ad platform is a place for marketers to bid to show ads to customers. This can be a problem for many agents because traders bid against each other, and their actions are interrelated. In the article, merchants and customers were grouped into different groups to reduce computational complexity. The agents state-space indicated the agents cost-revenue status, the action space was the (continuous) bid, and the reward was the customer clusters revenue.

Deep learning

More and more attempts to combine RL and other deep learning architectures can be seen recently and have shown impressive results.

One of RLs most influential jobs is Deepminds pioneering work to combine CNN with RL. In doing so, the agent can see the environment through high-dimensional sensors and then learn to interact with it.

CNN with RL are other combinations used by people to try new ideas. RNN is a type of neural network that has memories. When combined with RL, RNN offers agents the ability to memorize things. For example, they combined LSTM with RL to create a deep recurring Q network (DRQN) for playing Atari 2600 games. They also usedLSTM with RL to solve problems in optimizing chemical reactions.

Deepmind showed how to use generative models and RL to generate programs. In the model, the adversely trained agent used the signal as a reward for improving actions, rather than propagating gradients to the entry space as in GAN training. Incredible, isnt it?

Reinforcement is done with rewards according to the decisions made; it is possible to learn continuously from interactions with the environment at all times. With each correct action, we will have positive rewards and penalties for incorrect decisions. In the industry, this type of learning can help optimize processes, simulations, monitoring, maintenance, and the control of autonomous systems.

Some criteria can be used in deciding where to use reinforcement learning:

In addition to industry, reinforcement learning is used in various fields such as education, health, finance, image, and text recognition.

This article was written by Jair Ribeiro and was originally published on Towards Data Science. You can read it here.

Published October 27, 2020 10:49 UTC

Originally posted here:

What the hell is reinforcement learning and how does it work? - The Next Web

Artificial Intelligence (AI) is a field that requires no introduction. AI has ridden the tailcoats of Moores Law which states that the speed and capability of computers can be expected to double every two years. Since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a doubling every 3 to 4 months, with the end result that the amount of computing resources allocated to AI has grown by 300,000x since 2012. No other industry can compare with these growth statistics.

We will explore what fields of AI are leading this acceleration, what companies are best positioned to take advantage of this growth, and why it matters.

Machine learning is a subfield of AI which is essentially programming machines to learn. There are multiple types of machine learning algorithms, the most popular by far is deep learning, this involves feeding data into an Artificial Neural Network (ANN). An ANN is a very compute intensive network of mathematical functions joined together in a format inspired by the neural networks found in the human brain.

The more big data that is fed into an ANN, the more precise the ANN becomes. For example, if you are attempting to train an ANN to learn how to identify cat pictures, if you feed the network 1000 cat pictures the network will have a small level of accuracy of perhaps 70%, if you increase it to 10000 pictures, the level of accuracy may increase to 80%, if you increase it by 100000 pictures, then you have just increased the accuracy of the network to 90%, and onwards.

Herein lies one of the opportunities, companies that dominate the field of AI chip development are naturally ripe for growth.

There are many other types of machine learning that show promise, such as reinforcement learning, this is training an agent through the repetition of actions and associated rewards. By using reinforcement learning an AI system can compete against itself with the intention of improving how well it performs. For example, a program playing chess will play against itself repeatedly, with every instance of the gameplay improving how it performs in the next game.

Currently the best types of AI use a combination of both deep learning and reinforcement learning in what is commonly referred to as deep reinforcement learning. All of the leading AI companies in the world such as Tesla use some type of deep reinforcement learning.

While there are other types of important machine learning systems that are currently being advanced such as meta-learning, for the sake of simplicity deep learning and its more advanced cousin deep reinforcement learning are what investors should be most familiar with. The companies that are at the forefront of this technological advancement will be best positioned to take advantage of the huge exponential growth we are witnessing in AI.

If there is one differentiator between companies that will succeed, and become market leaders, and companies that will fail, it is big data. All types of machine learning are heavily reliant on data science, this is best described as a process of understanding the world from patterns in data. In this case the AI is learning from data, and the more data the more accurate the results. There are some exceptions to this rule due to what is called overfitting, but this is a concern that AI developers are aware of and take precautions to compensate for.

The importance of big data is why companies such as Tesla have a clear market advantage when it comes to autonomous vehicle technology. Every single Tesla that is in motion and using auto-pilot is feeding data into the cloud. This enables Tesla to use deep reinforcement learning, and other algorithm tweaks in order to improve the overall autonomous vehicle system.

This is also why companies such as Google will be so difficult for challengers to dethrone. Every day that goes by is a day that Google collects data from its myriad of products and services, this includes search results, Google Adsense, Android mobile device, the Chrome web browser, and even the Nest thermostat. Google is drowning is more data than any other company in the world. This is not even counting all of the moonshots they are involved in.

By understanding why deep learning and data science matters, we can ten infer why the companies below are so powerful.

There are three current market leaders that are going to be very difficult to challenge.

Alphabet Inc is the umbrella company for all Google products which includes the Google search engine. A short history lesson is necessary to explain why they are such a market leader in AI. In 2010, a British company DeepMind was launched with the goal of applying various machine learning techniques towards building general-purpose learning algorithms.

In 2013, DeepMind took the world by storm with various accomplishments including becoming world champion at seven Atari games by using deep reinforcement learning.

In 2014, Google acquired DeepMind for $500 Million, shortly thereafter in 2015 DeepMinds AlphaGo became the first AI program to defeat a professional human Go player, and the first program to defeat a Go world champion. For those who are unfamiliar Go is considered by many to be the most challenging game in existence.

DeepMind is currently considered a market leader in deep reinforcement learning, and Artificial General Intelligence (AGI), a futuristic type of AI with the goal of eventually achieving or surpassing human level intelligence.

We still need to factor in the other other types of AI that Google is currently involved in such as Waymo, a market leader in automonous vehicle technology, second only to Tesla, and the secretive AI systems currently used in the Google search engine.

Google is currently involved in so many levels of AI, that it would take an exhaustive paper to cover them all.

As previously stated Tesla is taking advantage of big data from its fleet of on-road vehicles to collect data from its auto-pilot. The more data that is collected the more it can improve using deep reinforcement, this is especially important for what are deemed as edge cases, this is known as scenarios that dont happen frequently in real-life.

For example, it is impossible to predict and program in every type of scenario that may happen on the road, such as a suitcase rolling into traffic, or a plane falling from the sky. In this case there is very little specific data, and the system needs to associate data from many different scenarios. This is another advantage of having a huge amount of data, while it may be the first time a Tesla in Houston encounters a scenario, it is possible that a Tesla in Dubai may have encountered something similar.

Tesla is also a market leader in battery technology, and in electric technology for vehicles. Both of these rely on AI systems to optimize the range of a vehicle before a recharge is required. Tesla is known for its frequent on-air updates with AI optimizations that improve by a few percentage points the performance and range of its vehicle fleet.

As if this was not sufficient, Tesla is also designing its own AI chips, this means it is no longer reliant on third-party chips, and they can optimize chips to work with their full self-driving software from the ground up.

NVIDIA is the company best positioned to take advantage of the current rise in demand in GPU (Graphics processing unit) chips, as they are currently responsible for 80% of all GPUsales.

While GPUs were initially used for video games, they were quickly adopted by the AI industry specifically for deep learning. The reason GPUs are so important is that the speed of AI computations is greatly enhanced when computations are carried out in parallel. While training a deep learning ANN, inputs are required and this depends heavily on matrix multiplications, where parallelism is important.

NVIDIA is constantly releasing new AI chips that are optimized for different use cases and requirements of AI researchers. It is this constant pressure to innovate that is maintaining NVIDIA as a market leader.

It is impossible to list all of the companies that are involved in some form of AI, what is important is understanding the machine learning technologies that are responsible for most of the innovation and growth that the industry has witnessed. We have highlighted 3 market leaders, many more will come along. To keep abreast of AI, you should stay current with AI news, avoid AI hype, and understand that this field is constantly evolving.

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Investing in Artificial Intelligence (AI) - Everything You Need to Know - Securities.io

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The world was on this brink of this fiery hell when Jordan Peterson read Solzhenitsyn and began to turn from despair toward hope.

It was Solzhenitsyn who most crucially made the case that the terrible excesses of Communism could not be conveniently blamed on the corruption of the Soviet leadership, the cult of personality surrounding Stalin, or the failure to put the otherwise stellar and admirable utopian principles of Marxism into proper practice. It was Solzhenitsyn who demonstrated that the death of millions and the devastation of many more were, instead, a direct causal consequence of the philosophy (worse, perhaps: the theology) driving the Communist system. The hypothetically egalitarian, universalist doctrines of Karl Marx contained hidden within them sufficient hatred, resentment, envy and denial of individual culpability and responsibility to produce nothing but poison and death when manifested in the world

An excerpt from Jordan Petersons foreword to the 50th Anniversary edition of Solzhenitsyns The Gulag Archipelago

Make no mistake, thems fightin words. This fierce sermon about the gospel written by one of mankinds greatest uncanonized saints, Aleksandr Solzhenitsyn, was an impetus for naming my biography of Dr. Peterson Savage Messiah. Under the guise of a mild-mannered college professor, Peterson preached the scripture of the prophets: bloody, accusatory, inflammatory, unflinching writings revolting to non-believers but manna to the faithful.

In Solzhenitsyn, Peterson found an eyewitness to the prophecy that hell on earth would reign in the 20th century as prophesied by Friedrich Nietzsche, another tormented and unsung saint of the Peterson catechism. When Nietzsche proclaimed God is Dead in 1882, he actually wrote:

God is dead. God remains dead. And we have killed him. How shall we comfort ourselves, the murderers of all murderers? What was holiest and mightiest of all that the world has yet owned has bled to death under our knives: who will wipe this blood off us. What water is there for us to clean ourselves?

Nietzsche was not gloating over the death of God as so many atheists celebrated. It was a warning, a curse. His vision accurately foreshadowed the coming 20th centurys depravities of Marxism, Communism, Socialism, Nazism, fascism, nihilism, world wars, race wars, mustard gas, Zyklon B, race lynchings, killing fields, concentration camps and gulags. All of them were creations of the 20th century when hate flourished, when haters found a place to hang their hate. Solzhenitsyn suffered through some of them personally. He survived to see all of them happen in his lifetime and refused to look away.

Solzhenitsyn eventually found solace and redemption in Christianity. He took crucified humanity down from its cross, laid it in a proper grave and carved its headstone. He identified the source of evil for a new generation and proclaimed it as the evil that runs through every human heart. For Nietzsche, the Enlightenment had killed God, supplanting his grace and wisdom with human pride and arrogance. Solzhenitsyn personally paid the price for this.

In Solzhenitsyns footsteps, young Jordan Peterson found the path that led away from the coming, ultimate human folly of the 20th century: mutually assured destruction, nuclear global annihilation. This pinnacle of fatal human arrogance finally revealed that hell was, indeed, now on earthand even admitted in its name that insanity was now official policy.

This terror had tormented young Peterson since grade school in the 1970s. He suffered nightmares of charred bodies, ravenous dogs, and a vaporized world of eternal winter. In 1974, Solzhenitsyns The Gulag Archipelago was published in the West. It had already been circulated hand-to-hand in secret, mimeographed copies, a death warrant if discovered. It corroded the foundations of the tottering Marxist Soviet Union with every new pair of hands that touched it. Eventually, it was instrumental in collapsing the Evil Empire. And like the prophet Jeremiahs Old Testament Book of Lamentations, it showed how Gods people had forsaken him, how they now worshipped idols like Joseph Stalin, and how they were prepared to burn their children in an offering to Moloch, the idol of child sacrifice.

The world was on this brink of this fiery hell when Jordan Peterson read Solzhenitsyn and began to turn from despair toward hope. Peterson had found the true enemy. It was not Russia. It was the inherent evil that ran through every human heart, as Solzhenitsyn said. With the help of Solzhenitsyn, Nietzsche, Carl Jung, child psychologist Jean Piaget and many others, Peterson began to crack the code that revealed the enemys strategy.

Stepping back from his own brink of hellish insanity, Peterson committed his life to healing human hearts and minds that had become infected with evil. He became a psychologist and social scientist. As he grew in his experience with severely mentally ill patients, he found ways of strengthening them against the many types of purposeful and random evil in the world. To university students, he began to teach what he had learned from these great prophets of the human struggle. He started with the ancient archetypes of good and evil that populated mankinds collective unconscious discovered by Carl Jung.

Then, following Solzhenitsyn more closely, Peterson began to use the Jewish and Christian Bible as the library of archetypes from our collective unconscious. He began at the beginning with the Book of Genesis and the logos, the word of God that went out over the waters and created order from chaos. Then, in the Garden of Eden, he saw the warning against the tempting snake of moral corruption, the resulting arrogance before God and its product, the fall of mankind. But, unlike Solzhenitsyn, Peterson continued to maintain his distance from a personal belief in God. In summary, he has said that he did not yet feel he had the personal understanding to believe in God. He just could not accept that God existed based on faith alone. He had not resolved the mystery of God for himself. But he was close.

Perhaps it is the level of suffering that eventually drives one to his knees in submission, in pleading for guidance from God. One suspects that was the case with Solzhenitsyn. New revelations point to the possibility that Peterson may have recently suffered enough to again follow closely in the footsteps of Solzhenitsyn, this time into the mystery of God.

For the past year and some months, Peterson has suffered the horrendous side-effects from the long-term prescribed use of benzodiazepines. Common trade names for this drug began with Librium, later became Valium, then Xanax. Now, there are nearly 100 other names. It is one of the most commonly prescribed drugs in the world and is considered to be a safe, minor tranquilizer. Yet, prescribed benzodiazepines might also be consideredas in Petersons own casean example of the random, inexplicable malevolence of life, like natural disasters, that stalk human beings along with human-generated evil.

Peterson has recently announced on YouTube that he is sufficiently healthy now to return to public life. In that video, he speaks of Gods grace and mercy that allowed him to survive and regain most of his mental and physical abilities. Again, we see that Solzhenitsyn may have been pivotal in leading Peterson away from madness and self-destruction, first as a young man rescued from nihilism and despair in the contemplation of nuclear holocaust and now as a grandfather redeemed from soul-destroying drug addiction.

As was noted at the beginning of this piece, Peterson wrote the foreword to the 50th anniversary authorized and abridged version of The Gulag Archipelago. As perhaps a testament to Petersons lifelong commitment to teaching the text and principles of Solzhenitsyns masterpiece, he received, the greatest honor of my life in being invited to write the foreword. It seems like the evil that runs through every human heart has mysteriously bound these two great minds together. What they have witnessed individuallyfrom the corrupted morality in human evil to the random malevolence extant in the world at-largehas brought them together in their private ways before God. May God and every human heart bless and cherish their lives, their memory, and our future together, thanks in part to them.

Jim Proseris the author of Savage Messiah: How Dr. Jordan Peterson Is Saving Western Civilization and No Better Friend, No Worse Enemy: The Life of General James Mattis.

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Jordan Peterson and the Return of Solzhenitsyn - Merion West

Randy Sharer can attest. My longtime colleague at The Pantagraph, Sharer interviewed Loy and saw him in action more than any other media member.

He watched Loy mentor a multitude of all-staters and All-Americans at the high school and club levels, marveling at his ability to connect with swimmers of all ages.

He was one of those guys who was just born to coach, Sharer said. He had that enthusiasm for every single kid. Even with the 12-and-under kids, he knew their times and what was a good time. It was never work to him it didnt seem like. He could just work forever and all of those practices where they get up so early in swimming, that was nothing to him because he loved it.

Its the only way you become the longest tenured coach at BHS. John Szabo, the Purple Raiders retired athletic director, has coached track and cross country at the school for 40 years. He said he ranks second to Loys 41 years as a BHS coach.

He touched a lot of lives in our community, Szabo said. He loved being around kids. He loved them and they always respected him and worked hard for him.

Current BHS athletic director Tony Bauman called Loys death a huge shock for all of us, adding, Trying to comprehend what he meant to everybody is saddening.

He impacted people in such a positive way, Bauman said. For athletes we have swimming now, some of their parents swam for Bob. To hear their stories and the way he impacted them and then to have him coach their kids, it was a meaningful thing for their families.

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Kindred: Bob Loys legacy? His lifes work will live on - The Pantagraph

WHAT the Meteorettes managed to do on the road at the weekend cannot be overstated.

To even the basketball layman, the numbers paint a compelling picture.

Those are 22 and 28 - the margins of victory over rivals Bundaberg and Gladstone.

Also 623 and 186 - kilometres covered in a bus, just to be there.

It amounted to two wins, from 120 gruelling minutes played, all inside 24 hours.

With just seven players to choose from.

In this era of competition, what Scott McKenzie's team achieved at the weekend should not have been possible.

While their rivals welcomed some temporary imports from the Sunshine Coast and Brisbane to bolster their ranks, the Meteorettes were forced to make do with a skeleton crew.

Their young guns travelled in the opposition direction, to Townsville, for U18 representative duties.

It left the senior Meteorettes with just two on the bench for their toughest road trip on the ConocoPhillips CQ Cup calendar.

And yet somehow, the Meteorettes got through unscathed. Not only that, but they dominated once again.

They defied the odds and expectation to again prove Mackay deserves to be considered one of the best female basketball programs in Queensland.

Jordan Peterson overcame an ankle injury to play a key role in the Meteorettes win over Gladstone on Sunday. Photo: Callum Dick

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"I was extremely nervous. I thought it was going to be a tough weekend for us," McKenzie admitted.

"We knew Bundaberg had brought in a player and Gladstone another couple. We only had seven."

Then Jordan Peterson went over on her ankle on Saturday night and the Meteorettes faced the very real prospect of rotating just one off the bench on Sunday.

"I asked on Sunday 'are you any good?' and she said 'I've strapped it up tight - I'm ready to go'," McKenzie recalled.

"When we were in a bit of a run on Sunday, she came in and made a difference for us.

"I was really proud of her effort this weekend."

Peterson's selfless act was one of a long line of gutsy performances from the seven-strong squad which flew the flag for Mackay at the weekend.

Not only will the winning road double be a big boost to the team's confidence, it should also strike fear in their rivals.

With the deck stacked against them, the Meteorettes proved too good.

It has the group well poised to continue toward its "ultimate goal", which is an inaugural CQ Cup crown and confirmation as the best.

"That's obviously the ultimate goal and we've put ourselves in a position now to do that," McKenzie said.

"Realistically if we come out next week and win at home, we'll sew up top spot. That gives us a home semi - win that, and it's a home grand final. That's always been the goal."

The Meteors and Meteorettes will enjoy a well-deserved bye this weekend, before returning to The Crater on November 7.

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