The Prometheus League
Breaking News and Updates
- Abolition Of Work
- Ai
- Alt-right
- Alternative Medicine
- Antifa
- Artificial General Intelligence
- Artificial Intelligence
- Artificial Super Intelligence
- Ascension
- Astronomy
- Atheism
- Atheist
- Atlas Shrugged
- Automation
- Ayn Rand
- Bahamas
- Bankruptcy
- Basic Income Guarantee
- Big Tech
- Bitcoin
- Black Lives Matter
- Blackjack
- Boca Chica Texas
- Brexit
- Caribbean
- Casino
- Casino Affiliate
- Cbd Oil
- Censorship
- Cf
- Chess Engines
- Childfree
- Cloning
- Cloud Computing
- Conscious Evolution
- Corona Virus
- Cosmic Heaven
- Covid-19
- Cryonics
- Cryptocurrency
- Cyberpunk
- Darwinism
- Democrat
- Designer Babies
- DNA
- Donald Trump
- Eczema
- Elon Musk
- Entheogens
- Ethical Egoism
- Eugenic Concepts
- Eugenics
- Euthanasia
- Evolution
- Extropian
- Extropianism
- Extropy
- Fake News
- Federalism
- Federalist
- Fifth Amendment
- Fifth Amendment
- Financial Independence
- First Amendment
- Fiscal Freedom
- Food Supplements
- Fourth Amendment
- Fourth Amendment
- Free Speech
- Freedom
- Freedom of Speech
- Futurism
- Futurist
- Gambling
- Gene Medicine
- Genetic Engineering
- Genome
- Germ Warfare
- Golden Rule
- Government Oppression
- Hedonism
- High Seas
- History
- Hubble Telescope
- Human Genetic Engineering
- Human Genetics
- Human Immortality
- Human Longevity
- Illuminati
- Immortality
- Immortality Medicine
- Intentional Communities
- Jacinda Ardern
- Jitsi
- Jordan Peterson
- Las Vegas
- Liberal
- Libertarian
- Libertarianism
- Liberty
- Life Extension
- Macau
- Marie Byrd Land
- Mars
- Mars Colonization
- Mars Colony
- Memetics
- Micronations
- Mind Uploading
- Minerva Reefs
- Modern Satanism
- Moon Colonization
- Nanotech
- National Vanguard
- NATO
- Neo-eugenics
- Neurohacking
- Neurotechnology
- New Utopia
- New Zealand
- Nihilism
- Nootropics
- NSA
- Oceania
- Offshore
- Olympics
- Online Casino
- Online Gambling
- Pantheism
- Personal Empowerment
- Poker
- Political Correctness
- Politically Incorrect
- Polygamy
- Populism
- Post Human
- Post Humanism
- Posthuman
- Posthumanism
- Private Islands
- Progress
- Proud Boys
- Psoriasis
- Psychedelics
- Putin
- Quantum Computing
- Quantum Physics
- Rationalism
- Republican
- Resource Based Economy
- Robotics
- Rockall
- Ron Paul
- Roulette
- Russia
- Sealand
- Seasteading
- Second Amendment
- Second Amendment
- Seychelles
- Singularitarianism
- Singularity
- Socio-economic Collapse
- Space Exploration
- Space Station
- Space Travel
- Spacex
- Sports Betting
- Sportsbook
- Superintelligence
- Survivalism
- Talmud
- Technology
- Teilhard De Charden
- Terraforming Mars
- The Singularity
- Tms
- Tor Browser
- Trance
- Transhuman
- Transhuman News
- Transhumanism
- Transhumanist
- Transtopian
- Transtopianism
- Ukraine
- Uncategorized
- Vaping
- Victimless Crimes
- Virtual Reality
- Wage Slavery
- War On Drugs
- Waveland
- Ww3
- Yahoo
- Zeitgeist Movement
-
Prometheism
-
Forbidden Fruit
-
The Evolutionary Perspective
Monthly Archives: March 2020
Zendesk, Inc. (ZEN) is primed for evolution with the beta value of 1.26 – The InvestChronicle
Posted: March 31, 2020 at 6:53 am
Lets start up with the current stock price of Zendesk, Inc. (ZEN), which is $64.85 to be very precise. The Stock rose vividly during the last session to $65.49 after opening rate of $65 while the lowest price it went was recorded $62.67 before closing at $67.01.
Zendesk, Inc. had a pretty Dodgy run when it comes to the market performance. The 1-year high price for the companys stock is recorded $94.89 on 07/11/19, with the lowest value was $50.23 for the same time period, recorded on 03/17/20.
Price records that include history of low and high prices in the period of 52 weeks can tell a lot about the stocks existing status and the future performance. Presently, Zendesk, Inc. shares are logging -31.66% during the 52-week period from high price, and 29.11% higher than the lowest price point for the same timeframe. The stocks price range for the 52-week period managed to maintain the performance between $50.23 and $94.89.
The companys shares, operating in the sector of technology managed to top a trading volume set approximately around 1.51 million for the day, which was evidently lower, when compared to the average daily volumes of the shares.
When it comes to the year-to-date metrics, the Zendesk, Inc. (ZEN) recorded performance in the market was -15.37%, having the revenues showcasing -16.45% on a quarterly basis in comparison with the same period year before. At the time of this writing, the total market value of the company is set at 7.60B, as it employees total of 3570 workers.
According to the data provided on Barchart.com, the moving average of the company in the 100-day period was set at 77.31, with a change in the price was noted -7.38. In a similar fashion, Zendesk, Inc. posted a movement of -10.22% for the period of last 100 days, recording 1,808,827 in trading volumes.
Total Debt to Equity Ratio (D/E) can also provide valuable insight into the companys financial health and market status. The debt to equity ratio can be calculated by dividing the present total liabilities of a company by shareholders equity. Debt to Equity thus makes a valuable metrics that describes the debt, company is using in order to support assets, correlating with the value of shareholders equity. The total Debt to Equity ratio for ZEN is recording 0.00 at the time of this writing. In addition, long term Debt to Equity ratio is set at 1.06.
Raw Stochastic average of Zendesk, Inc. in the period of last 50 days is set at 35.42%. The result represents downgrade in oppose to Raw Stochastic average for the period of the last 20 days, recording 47.54%. In the last 20 days, the companys Stochastic %K was 48.68% and its Stochastic %D was recorded 42.77%.
Considering, the past performance of Zendesk, Inc., multiple moving trends are noted. Year-to-date Price performance of the companys stock appears to be encouraging, given the fact the metric is recording -15.37%. Additionally, trading for the stock in the period of the last six months notably deteriorated by -12.46%, alongside a downfall of -22.17% for the period of the last 12 months. The shares increased approximately by 9.57% in the 7-day charts and went up by 14.01% in the period of the last 30 days. Common stock shares were lifted by -16.45% during last recorded quarter.
Read the rest here:
Zendesk, Inc. (ZEN) is primed for evolution with the beta value of 1.26 - The InvestChronicle
Posted in Evolution
Comments Off on Zendesk, Inc. (ZEN) is primed for evolution with the beta value of 1.26 – The InvestChronicle
Bellerophon Therapeutics, Inc. (BLPH) is primed for evolution with the beta value of -1.34 – The InvestChronicle
Posted: at 6:53 am
Bellerophon Therapeutics, Inc. (BLPH) is priced at $12.60 after the most recent trading session. At the very opening of the session, the stock price was $20.31 and reached a high price of $21, prior to closing the session it reached the value of $12.67. The stock touched a low price of $12.15.
Bellerophon Therapeutics, Inc. had a pretty favorable run when it comes to the market performance. The 1-year high price for the companys stock is recorded $26.00 on 03/20/20, with the lowest value was $3.19 for the same time period, recorded on 03/19/20.
Price records that include history of low and high prices in the period of 52 weeks can tell a lot about the stocks existing status and the future performance. Presently, Bellerophon Therapeutics, Inc. shares are logging -51.54% during the 52-week period from high price, and 295.49% higher than the lowest price point for the same timeframe. The stocks price range for the 52-week period managed to maintain the performance between $3.19 and $26.00.
The companys shares, operating in the sector of healthcare managed to top a trading volume set approximately around 4.46 million for the day, which was evidently lower, when compared to the average daily volumes of the shares.
When it comes to the year-to-date metrics, the Bellerophon Therapeutics, Inc. (BLPH) recorded performance in the market was 141.54%, having the revenues showcasing 136.07% on a quarterly basis in comparison with the same period year before. At the time of this writing, the total market value of the company is set at 52.07M, as it employees total of 17 workers.
During the last month, 1 analysts gave the Bellerophon Therapeutics, Inc. a BUY rating, 0 of the polled analysts branded the stock as an OVERWEIGHT, 0 analysts were recommending to HOLD this stock, 0 of them gave the stock UNDERWEIGHT rating, and 0 of the polled analysts provided SELL rating.
According to the data provided on Barchart.com, the moving average of the company in the 100-day period was set at 6.36, with a change in the price was noted +6.00. In a similar fashion, Bellerophon Therapeutics, Inc. posted a movement of +91.88% for the period of last 100 days, recording 528,386 in trading volumes.
Total Debt to Equity Ratio (D/E) can also provide valuable insight into the companys financial health and market status. The debt to equity ratio can be calculated by dividing the present total liabilities of a company by shareholders equity. Debt to Equity thus makes a valuable metrics that describes the debt, company is using in order to support assets, correlating with the value of shareholders equity. The total Debt to Equity ratio for BLPH is recording 0.00 at the time of this writing. In addition, long term Debt to Equity ratio is set at 0.00.
Raw Stochastic average of Bellerophon Therapeutics, Inc. in the period of last 50 days is set at 40.91%. The result represents downgrade in oppose to Raw Stochastic average for the period of the last 20 days, recording 40.91%. In the last 20 days, the companys Stochastic %K was 39.45% and its Stochastic %D was recorded 37.25%.
If we look into the earlier routines of Bellerophon Therapeutics, Inc., multiple moving trends are noted. Year-to-date Price performance of the companys stock appears to be pessimistic, given the fact the metric is recording 141.54%. Additionally, trading for the stock in the period of the last six months notably improved by 68.93%, alongside a boost of 30.11% for the period of the last 12 months. The shares increased approximately by 1.77% in the 7-day charts and went down by -29.61% in the period of the last 30 days. Common stock shares were driven by 136.07% during last recorded quarter.
Go here to see the original:
Posted in Evolution
Comments Off on Bellerophon Therapeutics, Inc. (BLPH) is primed for evolution with the beta value of -1.34 – The InvestChronicle
Twilio Inc. (TWLO) is primed for evolution with the beta value of 1.12 – The InvestChronicle
Posted: at 6:53 am
Lets start up with the current stock price of Twilio Inc. (TWLO), which is $96.63 to be very precise. The Stock rose vividly during the last session to $98.96 after opening rate of $98 while the lowest price it went was recorded $95.51 before closing at $100.69.
Twilio Inc. had a pretty Dodgy run when it comes to the market performance. The 1-year high price for the companys stock is recorded $151.00 on 06/20/19, with the lowest value was $68.06 for the same time period, recorded on 03/16/20.
Price records that include history of low and high prices in the period of 52 weeks can tell a lot about the stocks existing status and the future performance. Presently, Twilio Inc. shares are logging -36.01% during the 52-week period from high price, and 41.99% higher than the lowest price point for the same timeframe. The stocks price range for the 52-week period managed to maintain the performance between $68.06 and $151.00.
The companys shares, operating in the sector of technology managed to top a trading volume set approximately around 2.19 million for the day, which was evidently lower, when compared to the average daily volumes of the shares.
When it comes to the year-to-date metrics, the Twilio Inc. (TWLO) recorded performance in the market was -1.68%, having the revenues showcasing -5.83% on a quarterly basis in comparison with the same period year before. At the time of this writing, the total market value of the company is set at 14.07B, as it employees total of 1331 workers.
During the last month, 22 analysts gave the Twilio Inc. a BUY rating, 2 of the polled analysts branded the stock as an OVERWEIGHT, 3 analysts were recommending to HOLD this stock, 0 of them gave the stock UNDERWEIGHT rating, and 0 of the polled analysts provided SELL rating.
According to the data provided on Barchart.com, the moving average of the company in the 100-day period was set at 105.71, with a change in the price was noted -0.43. In a similar fashion, Twilio Inc. posted a movement of -0.44% for the period of last 100 days, recording 3,392,462 in trading volumes.
Total Debt to Equity Ratio (D/E) can also provide valuable insight into the companys financial health and market status. The debt to equity ratio can be calculated by dividing the present total liabilities of a company by shareholders equity. Debt to Equity thus makes a valuable metrics that describes the debt, company is using in order to support assets, correlating with the value of shareholders equity. The total Debt to Equity ratio for TWLO is recording 0.11 at the time of this writing. In addition, long term Debt to Equity ratio is set at 0.11.
Raw Stochastic average of Twilio Inc. in the period of last 50 days is set at 44.00%. The result represents downgrade in oppose to Raw Stochastic average for the period of the last 20 days, recording 61.86%. In the last 20 days, the companys Stochastic %K was 64.64% and its Stochastic %D was recorded 61.38%.
Considering, the past performance of Twilio Inc., multiple moving trends are noted. Year-to-date Price performance of the companys stock appears to be encouraging, given the fact the metric is recording -1.68%. Additionally, trading for the stock in the period of the last six months notably deteriorated by -12.87%, alongside a downfall of -23.14% for the period of the last 12 months. The shares increased approximately by 14.26% in the 7-day charts and went up by 14.84% in the period of the last 30 days. Common stock shares were lifted by -5.83% during last recorded quarter.
See the original post:
Twilio Inc. (TWLO) is primed for evolution with the beta value of 1.12 - The InvestChronicle
Posted in Evolution
Comments Off on Twilio Inc. (TWLO) is primed for evolution with the beta value of 1.12 – The InvestChronicle
Coronavirus a product of evolution, may have been in humans for years: Study – The Kashmir Monitor
Posted: at 6:53 am
As the coronavirus pandemic continues to evolve, infecting millions worldwide, a team of scientists has discovered that Covid-19 could have spread among humans for years or even decades before now.
According to findings published in the journal Nature Medicine, the virus could have possibly been transmitted from animals to humans much before it was first detected in Wuhan province of China. In fact, there are speculations that it could have been as long as a decade.
The study, which was conducted by an international team of scientists from Australia, Britain and the US, was released on March 17 in the scientific journal.
Then, as a result of gradual evolutionary changes over years or perhaps decades. The virus eventually gained the ability to spread from human to human and cause serious, often life-threatening disease, Dr Francis Collins, director of the US National Institute of Health, said in an article published on the institutes website.
The study was conducted by Kristian Andersen from the Scripps Research Institute in California, Andrew Rambaut from the University of Edinburgh in Scotland, Ian Lipkin from Columbia University in New York, Edward Holmes from the University of Sydney, and Robert Garry from Tulane University in New Orleans.
By comparing the available genome sequence data for known coronavirus strains, we can firmly determine that SARS-CoV-2 originated through natural processes, said the lead researcher Kristian Andersen.
Besides them, Italian professor Giuseppe Remuzzi had pointed at strange pneumonias in Italy since last November. This could mean that Covid-19, which proved to be equally fatal in the country, could have reached Europe before anyone knew about it.
Prof Remuzzi, director of the Mario Negri Institute for Pharmacological Research in Milan, said he would not be surprised if some asymptomatic carriers had travelled around China or even abroad before December last year.
The unusual cases of pneumonia in November and December could mean that virus was already spreading in Lombardy, Italys worst-hit region, before Wuhan made headlines, he said.
Speaking on similar lines, a Chinese doctor working, who is treating Covid-19 patients in Beijing, said numerous cases of mysterious pneumonia outbreaks had been reported by health professionals across several countries last year.
There will be a day when the whole thing comes to light, the doctor said, preferring anonymity.
Doctors in Wuhan also noticed a surge in the number of pneumonia cases in December. Tests for flu and other pathogens returned negative.
Shortly after the epidemic erupted, Chinese scientists sequenced the genome of SARS-CoV-2 and made the data available to researchers worldwide. The resulting genomic sequence data showed that Chinese authorities had promptly detected the epidemic and that the number of cases were rising because of human to human transmission after a single introduction into the human population.
While an unknown strain was isolated, a team from the Wuhan Institute of Virology led by Shi Zhengli traced its origin to a bat virus found in a mountain cave close to the China-Myanmar border.
The virus has now infected every corner of the globe.
Go here to read the rest:
Coronavirus a product of evolution, may have been in humans for years: Study - The Kashmir Monitor
Posted in Evolution
Comments Off on Coronavirus a product of evolution, may have been in humans for years: Study – The Kashmir Monitor
Boyd Gaming Corporation (BYD) is primed for evolution with the beta value of 2.33 – The InvestChronicle
Posted: at 6:53 am
Boyd Gaming Corporation (BYD) is priced at $15.30 after the most recent trading session. At the very opening of the session, the stock price was $15.94 and reached a high price of $16.94, prior to closing the session it reached the value of $16.92. The stock touched a low price of $15.02.
Boyd Gaming Corporation had a pretty Dodgy run when it comes to the market performance. The 1-year high price for the companys stock is recorded $36.22 on 02/21/20, with the lowest value was $6.44 for the same time period, recorded on 03/18/20.
Price records that include history of low and high prices in the period of 52 weeks can tell a lot about the stocks existing status and the future performance. Presently, Boyd Gaming Corporation shares are logging -57.76% during the 52-week period from high price, and 137.58% higher than the lowest price point for the same timeframe. The stocks price range for the 52-week period managed to maintain the performance between $6.44 and $36.22.
The companys shares, operating in the sector of services managed to top a trading volume set approximately around 1.71 million for the day, which was evidently lower, when compared to the average daily volumes of the shares.
When it comes to the year-to-date metrics, the Boyd Gaming Corporation (BYD) recorded performance in the market was -48.90%, having the revenues showcasing -50.05% on a quarterly basis in comparison with the same period year before. At the time of this writing, the total market value of the company is set at 1.89B, as it employees total of 24300 workers.
During the last month, 10 analysts gave the Boyd Gaming Corporation a BUY rating, 0 of the polled analysts branded the stock as an OVERWEIGHT, 3 analysts were recommending to HOLD this stock, 0 of them gave the stock UNDERWEIGHT rating, and 0 of the polled analysts provided SELL rating.
According to the data provided on Barchart.com, the moving average of the company in the 100-day period was set at 27.38, with a change in the price was noted -12.09. In a similar fashion, Boyd Gaming Corporation posted a movement of -44.14% for the period of last 100 days, recording 1,493,556 in trading volumes.
Total Debt to Equity Ratio (D/E) can also provide valuable insight into the companys financial health and market status. The debt to equity ratio can be calculated by dividing the present total liabilities of a company by shareholders equity. Debt to Equity thus makes a valuable metrics that describes the debt, company is using in order to support assets, correlating with the value of shareholders equity. The total Debt to Equity ratio for BYD is recording 2.98 at the time of this writing. In addition, long term Debt to Equity ratio is set at 2.96.
Raw Stochastic average of Boyd Gaming Corporation in the period of last 50 days is set at 29.75%. The result represents downgrade in oppose to Raw Stochastic average for the period of the last 20 days, recording 43.28%. In the last 20 days, the companys Stochastic %K was 44.68% and its Stochastic %D was recorded 40.08%.
If we look into the earlier routines of Boyd Gaming Corporation, multiple moving trends are noted. Year-to-date Price performance of the companys stock appears to be encouraging, given the fact the metric is recording -48.90%. Additionally, trading for the stock in the period of the last six months notably deteriorated by -37.11%, alongside a downfall of -44.00% for the period of the last 12 months. The shares increased approximately by 10.79% in the 7-day charts and went up by 31.90% in the period of the last 30 days. Common stock shares were lifted by -50.05% during last recorded quarter.
Excerpt from:
Posted in Evolution
Comments Off on Boyd Gaming Corporation (BYD) is primed for evolution with the beta value of 2.33 – The InvestChronicle
How Will Marketing Strategies Evolve in Times of Coronavirus (COVID-19)? – MarTech Advisor
Posted: at 6:53 am
COVID-19 has impacted countries all around the world. With no cure in sight, social distancing is the only way to limit the spread of the virus. But during these times when people are staying home and working remotely, buying patterns have been drastically altered. How will marketing strategies evolve in these uncertain times and going forward?
Winning CX will come from Brands who can balance relevance, consistency and convenience to drive engagement. The kind of engagement that drives optimal customer lifetime value and real business impact.
Coronavirus is, by far, one of the biggest challenges this generation has faced, and its impacts will last in the years to come. The clinical trials for candidate vaccines have begun. But, for now, social distancing and remote work are the only solutions that can help people stay safe. In short, COVID-19 has people staying home, which has altered consumer buying patterns. We now see more of:
Moreover, each crisis leaves a long-term psychological impact on customers. While some might play it safe for a long time, others may want to indulge as a rebound.
Lynne Clement, client success specialist at ApexDrop, exclusively told MarTech Advisor,
Consumer attitudes and behaviors are changing. Marketers must start there. Life before COVID-19 revolved around going to work, grocery stores, malls, gyms, restaurants, movies, and schools. During COVID-19, we'll adapt to working, shopping, exercising, entertainment, and learning at home. Digital experiences will replace in-person experiences. Some of the new habits will become the new normal even after the crisis passes. Looking ahead, the best companies in the world will prepare now for how to meet the needs of consumers (where they're at) when the recovery period begins. Don't expect consumers will return to the same place.
Learn More: 20 Expert WFH Tips for Marketers in the Coronavirus (COVID-19) Outbreak
As social distancing becomes the new normal and organizations implement remote work what will the role of marketers look like? John Nash, chief marketing and strategy officer, Redpoint, exclusively told MarTech Advisor,
During a time of economic crisis, the role of the marketer may now seem obsolete. However, it is quite the opposite, as this is an opportunity to pick up on consumer behavior changes and virtually engage with consumers in new ways. These changing times make it crucial for marketers to see consumers as individuals and not group customers into segments. By relying on real-time data and providing brands with a Golden Record of all that is knowable about a customer (e.g. identities, transactions, behaviors), marketers can assist brands now more than ever in creating informed interactions. With these personalized insights, customers will benefit from relevant, positive engagement that is consistent regardless of distance, device, or journey path that is bound to vary for each individual consumer.
How should your marketing strategy evolve in such turbulent times?
To survive the negative sentiment and economic slowdown: resilience, innovation, agility, and empathy should be marketers' tools. As consumers' lifestyles are adapting to staying at home, marketers must proactively reach out to them where they are. We spoke to some marketing experts, and also got their insights on the evolution of marketing strategies.
Here is how you can adjust the sails of your marketing strategy:
Social distancing can lead to stress, boredom, anxiety, and a sense of loneliness. This makes it crucial to identify individual consumer needs and address them before it becomes a problem area.
For example, with schools and offices closed, it becomes difficult for parents to work from home and engage their kids. So Audible, an audiobook service by Amazon, is now offering free streaming of stories to entertain, teach, and engage children while schools remain shut.
During this unprecedented time, as consumers' buying behaviors and media consumption change, it's so important for marketers to understand these emerging patterns as well as to anticipate consumer needs. Real-time insights and technology that anticipate consumer interests and needs are essential for marketers and brands to create meaningful, supportive engagement with consumers. Brands have an opportunity to deliver real-time assurance, a feeling of connection.
~ Carrie Parker, VP marketing, Valassis, said exclusively to MarTech Advisor
It has become a challenge to meet customer needs, and you must think about how best to serve customers with limited resources and social distancing constraints. Organizations, especially e-commerce giants are over-burdened with rising online orders, stocks running out, prioritizing essentials, and ensuring the health of employees and customers.
Amazon has taken numerous measures to support customers, employees, and communities. From ramping up fulfillment and delivery hiring to ensuring fair pricing, adjusting shopping hours for senior customers, and adjusting delivery (like the option to choose No-Rush Shipping) and logistics for prioritizing essentials, Amazon is trying to cater to customer requirements. Customers can also select 'Unattended Delivery' options to avoid coming in contact with the delivery agents. Amazon has also pledged to donate 250,000 essential items to Seattle's quarantine patients. Apart from this, Amazon Care, will pick-up and deliver COVID-19 tests in the Seattle area, as reported by CNBC.
All marketing efforts must now be reframed through the lens of COVID-19 and its broader business impact...many products can still play a role in this new reality. Focus on increased personalized connectivity and ways to keep services running. You may not be able to visit your favorite retail store or restaurant, but the virtual community created online mixed with creative new delivery and commerce services may be a realistic alternative. Technology should then serve as the backbone to deliver these ideas quickly.
~ Paula Hansen, chief revenue officer, SAP Customer Experience, said exclusively to MarTech Advisor
As people stay at home, time spent on their mobile devices, and online platforms is already on the rise. They are spending more time on OTT streaming platforms for entertainment, social media for connecting with the outside world, e-commerce portals for shopping, and so on. Although COVID-19 has disrupted marketing and advertising initiatives, we can expect marketers to keep their plans fluid and tweak their ad-spends to reach customers where they are.
McDonald's has started diverting media spend to safer options like McDelivery and Drive Through, Eugene Lee, Marketing Director, Asia Region, disclosed to TheStar.com. The company is adjusting marketing budgets as consumers consume more digital media and practice social distancing. McDonald's is also stressing on quality, safety, cleanliness, and promoting its McDelivery and Drive-Thru services through advertising campaigns to focus on consumer safety.
Robert Rothschild, VP, global head of marketing, Smartly.io, when talking exclusively to MarTech Advisor about adjusting advertising budgets said,
We're in the beginning of a major behavioral shift, and as a result, some merchants may see e-commerce sales similar to the rates of Black Friday and Cyber Monday. That said, brands must reallocate budgets to ad campaigns accordingly, and reach consumers where they are most active Facebook, Instagram, Twitter, and Pinterest. According to November research from Smartly.io, 52 percent of retail marketers said they will spend more on social advertising than they did in 2019, and 50 percent were planning to spend at least half of their annual marketing budget on social media advertising this year. We anticipate this number will only increase as people stay indoors and social media advertising becomes a primary focus for retail brands.
Learn More: How Marketing Leaders Can Manage the Impact of Coronavirus (COVID-19) on Revenue
In worrying time likes these, brands must focus on instilling confidence in customers, by providing them with the means to cope with the situation, furnishing the right information, and going beyond to help customers. Being empathetic and prioritizing customer experience will help your brand stand out and help you build a loyal customer base.
Technology giants, Facebook, Google, Microsoft, Twitter, LinkedIn, Reddit, and YouTube, have joined hands to support and work together to help governments arrest the novel coronavirus with their technological and financial might, and also assist scientists in research to develop a cure. They also issued a joint statement to help combat fraud and misinformation.
Shep Hyken, Customer Service and Experience Expert wrote in his LinkedIn post, "Many businesses are going to suffer financially because of this. Rather than deteriorate in front of your customers, show how you're there for them through thick and thin. They shouldn't notice an interruption in the way they've always done business with you. That may mean you can't cut in all the places you want to cut. You may even need to intensify your efforts to ensure you deliver the best experience."
Quarantine and lockdown measures can help keep people safe and healthy. But don't forget the impact on mental health on different age groups. While kids cannot go to schools or play, the earning group is worried about finances, and seniors who are seemingly the most susceptible to the virus are stressed. It is, therefore, important to engage and entertain people. In these times, if marketers can find innovative ways to ensure their services reach customers, it will go a long way in building lasting relationships.
We are now seeing influencers partnering with fitness apps to help people stay fit at home, education apps to enable learning, gaming apps to engage people. For example, to support its community, Vogue has offered three months free access to all its digital titles, including a free issue of Vanity Fair dedicated to Milan.
"The immediate marketing challenge for companies is building a relationship beyond what they sell and focusing on helping, not selling. The focus changes from the product to how you can support users in getting through this. Cause-related options can be used to build visibility. Acknowledging changing consumer behavior and offering help is relevant. Empathy is an important tone," says Lynne Clement.
Learn More: Your 2020 Crisis Marketing Strategy: Marketing Communication and the Coronavirus (COVID-19)
The world is going to look different on the other side of COVID-19 as teams strengthen their muscles for creating engaging, targeted digital experiences for their buyers. CMOs that spend this time building end-to-end, personalized digital strategies are going to come out the other side with a huge competitive advantage over the ones that are simply adapting their live events strategies to virtual ones. Additionally, now more than ever, we're seeing marketing teams adapt rapidly to customer retention and expansion strategies. While customer acquisition programs might be thrown for a loop, marketers are recognizing that focusing on keeping their current customers happy and successful will help them weather this period.
~ Justin Keller, VP of marketing, Terminus, said exclusively to MarTech Advisor
No one can predict what's next in the global pandemic and when it will end. But, to survive and rise, we must stay sharp. And for that, you need to:
On that note, we leave you with the words of UN Secretary-General Antnio Guterres, "We are in this together and we will get through this, together."
How have you tweaked your marketing strategy to fight COVID-19? Tell us on Twitter, LinkedIn, or Facebook; we're always listening!
View post:
How Will Marketing Strategies Evolve in Times of Coronavirus (COVID-19)? - MarTech Advisor
Posted in Evolution
Comments Off on How Will Marketing Strategies Evolve in Times of Coronavirus (COVID-19)? – MarTech Advisor
Amgen Inc. (AMGN) is primed for evolution with the beta value of 0.97 – The InvestChronicle
Posted: at 6:52 am
At the end of the latest market close, Amgen Inc. (AMGN) was valued at $198.49. In that particular session, Stock kicked-off at the price of $194.42 while reaching the peak value of $205.71 and lowest value recorded on the day was $194.16. The stock current value is $198.27.
Amgen Inc. had a pretty favorable run when it comes to the market performance. The 1-year high price for the companys stock is recorded $244.99 on 12/17/19, with the lowest value was $166.30 for the same time period, recorded on 05/15/19.
Price records that include history of low and high prices in the period of 52 weeks can tell a lot about the stocks existing status and the future performance. Presently, Amgen Inc. shares are logging -19.07% during the 52-week period from high price, and 19.22% higher than the lowest price point for the same timeframe. The stocks price range for the 52-week period managed to maintain the performance between $166.30 and $244.99.
The companys shares, operating in the sector of healthcare managed to top a trading volume set approximately around 4.09 million for the day, which was evidently higher, when compared to the average daily volumes of the shares.
When it comes to the year-to-date metrics, the Amgen Inc. (AMGN) recorded performance in the market was -17.75%, having the revenues showcasing -18.04% on a quarterly basis in comparison with the same period year before. At the time of this writing, the total market value of the company is set at 117.07B, as it employees total of 23400 workers.
During the last month, 13 analysts gave the Amgen Inc. a BUY rating, 1 of the polled analysts branded the stock as an OVERWEIGHT, 12 analysts were recommending to HOLD this stock, 1 of them gave the stock UNDERWEIGHT rating, and 0 of the polled analysts provided SELL rating.
According to the data provided on Barchart.com, the moving average of the company in the 100-day period was set at 223.88, with a change in the price was noted -19.68. In a similar fashion, Amgen Inc. posted a movement of -9.03% for the period of last 100 days, recording 2,818,357 in trading volumes.
Total Debt to Equity Ratio (D/E) can also provide valuable insight into the companys financial health and market status. The debt to equity ratio can be calculated by dividing the present total liabilities of a company by shareholders equity. Debt to Equity thus makes a valuable metrics that describes the debt, company is using in order to support assets, correlating with the value of shareholders equity. The total Debt to Equity ratio for AMGN is recording 3.09 at the time of this writing. In addition, long term Debt to Equity ratio is set at 2.79.
Raw Stochastic average of Amgen Inc. in the period of last 50 days is set at 31.77%. The result represents downgrade in oppose to Raw Stochastic average for the period of the last 20 days, recording 53.94%. In the last 20 days, the companys Stochastic %K was 49.78% and its Stochastic %D was recorded 48.23%.
Considering, the past performance of Amgen Inc., multiple moving trends are noted. Year-to-date Price performance of the companys stock appears to be encouraging, given the fact the metric is recording -17.75%. Additionally, trading for the stock in the period of the last six months notably improved by 1.48%, alongside a boost of 5.66% for the period of the last 12 months. The shares increased approximately by 1.48% in the 7-day charts and went up by 5.32% in the period of the last 30 days. Common stock shares were lifted by -18.04% during last recorded quarter.
Read more from the original source:
Amgen Inc. (AMGN) is primed for evolution with the beta value of 0.97 - The InvestChronicle
Posted in Evolution
Comments Off on Amgen Inc. (AMGN) is primed for evolution with the beta value of 0.97 – The InvestChronicle
ON Semiconductor Corporation (ON) is primed for evolution with the beta value of 2.17 – The InvestChronicle
Posted: at 6:52 am
ON Semiconductor Corporation (ON) is priced at $12.89 after the most recent trading session. At the very opening of the session, the stock price was $13.5 and reached a high price of $13.5, prior to closing the session it reached the value of $14.16. The stock touched a low price of $12.85.
ON Semiconductor Corporation had a pretty Dodgy run when it comes to the market performance. The 1-year high price for the companys stock is recorded $25.92 on 01/14/20, with the lowest value was $8.17 for the same time period, recorded on 03/18/20.
Price records that include history of low and high prices in the period of 52 weeks can tell a lot about the stocks existing status and the future performance. Presently, ON Semiconductor Corporation shares are logging -50.27% during the 52-week period from high price, and 57.77% higher than the lowest price point for the same timeframe. The stocks price range for the 52-week period managed to maintain the performance between $8.17 and $25.92.
The companys shares, operating in the sector of technology managed to top a trading volume set approximately around 9.33 million for the day, which was evidently lower, when compared to the average daily volumes of the shares.
When it comes to the year-to-date metrics, the ON Semiconductor Corporation (ON) recorded performance in the market was -47.13%, having the revenues showcasing -47.49% on a quarterly basis in comparison with the same period year before. At the time of this writing, the total market value of the company is set at 5.82B, as it employees total of 34800 workers.
During the last month, 13 analysts gave the ON Semiconductor Corporation a BUY rating, 1 of the polled analysts branded the stock as an OVERWEIGHT, 9 analysts were recommending to HOLD this stock, 1 of them gave the stock UNDERWEIGHT rating, and 2 of the polled analysts provided SELL rating.
According to the data provided on Barchart.com, the moving average of the company in the 100-day period was set at 20.81, with a change in the price was noted -8.18. In a similar fashion, ON Semiconductor Corporation posted a movement of -38.82% for the period of last 100 days, recording 7,392,593 in trading volumes.
Total Debt to Equity Ratio (D/E) can also provide valuable insight into the companys financial health and market status. The debt to equity ratio can be calculated by dividing the present total liabilities of a company by shareholders equity. Debt to Equity thus makes a valuable metrics that describes the debt, company is using in order to support assets, correlating with the value of shareholders equity. The total Debt to Equity ratio for ON is recording 1.09 at the time of this writing. In addition, long term Debt to Equity ratio is set at 0.87.
Raw Stochastic average of ON Semiconductor Corporation in the period of last 50 days is set at 26.73%. The result represents downgrade in oppose to Raw Stochastic average for the period of the last 20 days, recording 43.07%. In the last 20 days, the companys Stochastic %K was 52.71% and its Stochastic %D was recorded 51.76%.
Lets take a glance in the erstwhile performances of ON Semiconductor Corporation, multiple moving trends are noted. Year-to-date Price performance of the companys stock appears to be encouraging, given the fact the metric is recording -47.13%. Additionally, trading for the stock in the period of the last six months notably deteriorated by -32.50%, alongside a downfall of -36.41% for the period of the last 12 months. The shares increased approximately by 5.62% in the 7-day charts and went up by 19.13% in the period of the last 30 days. Common stock shares were lifted by -47.49% during last recorded quarter.
Go here to read the rest:
Posted in Evolution
Comments Off on ON Semiconductor Corporation (ON) is primed for evolution with the beta value of 2.17 – The InvestChronicle
The Travelers Companies, Inc. (TRV) is primed for evolution with the beta value of 1.02 – The InvestChronicle
Posted: at 6:52 am
At the end of the latest market close, The Travelers Companies, Inc. (TRV) was valued at $98.67. In that particular session, Stock kicked-off at the price of $94.87 while reaching the peak value of $103.01 and lowest value recorded on the day was $94.81. The stock current value is $99.95.
The Travelers Companies, Inc. had a pretty Dodgy run when it comes to the market performance. The 1-year high price for the companys stock is recorded $155.09 on 07/16/19, with the lowest value was $76.99 for the same time period, recorded on 03/18/20.
Price records that include history of low and high prices in the period of 52 weeks can tell a lot about the stocks existing status and the future performance. Presently, The Travelers Companies, Inc. shares are logging -35.55% during the 52-week period from high price, and 29.82% higher than the lowest price point for the same timeframe. The stocks price range for the 52-week period managed to maintain the performance between $76.99 and $155.09.
The companys shares, operating in the sector of financial managed to top a trading volume set approximately around 3.06 million for the day, which was evidently higher, when compared to the average daily volumes of the shares.
When it comes to the year-to-date metrics, the The Travelers Companies, Inc. (TRV) recorded performance in the market was -27.02%, having the revenues showcasing -26.66% on a quarterly basis in comparison with the same period year before. At the time of this writing, the total market value of the company is set at 25.66B, as it employees total of 30800 workers.
During the last month, 3 analysts gave the The Travelers Companies, Inc. a BUY rating, 1 of the polled analysts branded the stock as an OVERWEIGHT, 12 analysts were recommending to HOLD this stock, 0 of them gave the stock UNDERWEIGHT rating, and 6 of the polled analysts provided SELL rating.
According to the data provided on Barchart.com, the moving average of the company in the 100-day period was set at 129.10, with a change in the price was noted -30.77. In a similar fashion, The Travelers Companies, Inc. posted a movement of -23.54% for the period of last 100 days, recording 1,836,101 in trading volumes.
Total Debt to Equity Ratio (D/E) can also provide valuable insight into the companys financial health and market status. The debt to equity ratio can be calculated by dividing the present total liabilities of a company by shareholders equity. Debt to Equity thus makes a valuable metrics that describes the debt, company is using in order to support assets, correlating with the value of shareholders equity. The total Debt to Equity ratio for TRV is recording 0.25 at the time of this writing. In addition, long term Debt to Equity ratio is set at 0.23.
Raw Stochastic average of The Travelers Companies, Inc. in the period of last 50 days is set at 35.39%. The result represents downgrade in oppose to Raw Stochastic average for the period of the last 20 days, recording 42.08%. In the last 20 days, the companys Stochastic %K was 36.55% and its Stochastic %D was recorded 31.74%.
Now, considering the stocks previous presentation, multiple moving trends are noted. Year-to-date Price performance of the companys stock appears to be encouraging, given the fact the metric is recording -27.02%. Additionally, trading for the stock in the period of the last six months notably deteriorated by -32.51%, alongside a downfall of -26.99% for the period of the last 12 months. The shares increased approximately by 1.59% in the 7-day charts and went up by 11.66% in the period of the last 30 days. Common stock shares were lifted by -26.66% during last recorded quarter.
The rest is here:
Posted in Evolution
Comments Off on The Travelers Companies, Inc. (TRV) is primed for evolution with the beta value of 1.02 – The InvestChronicle
What is machine learning? Everything you need to know | ZDNet
Posted: at 6:51 am
Machine learning is enabling computers to tackle tasks that have, until now, only been carried out by people.
From driving cars to translating speech, machine learning is driving an explosion in the capabilities of artificial intelligence -- helping software make sense of the messy and unpredictable real world.
But what exactly is machine learning and what is making the current boom in machine learning possible?
At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data.
Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video.
The key difference from traditional computer software is that a human developer hasn't written code that instructs the system how to tell the difference between the banana and the apple.
Instead a machine-learning model has been taught how to reliably discriminate between the fruits by being trained on a large amount of data, in this instance likely a huge number of images labelled as containing a banana or an apple.
Data, and lots of it, is the key to making machine learning possible.
Machine learning may have enjoyed enormous success of late, but it is just one method for achieving artificial intelligence.
At the birth of the field of AI in the 1950s, AI was defined as any machine capable of performing a task that would typically require human intelligence.
AI systems will generally demonstrate at least some of the following traits: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity.
Alongside machine learning, there are various other approaches used to build AI systems, including evolutionary computation, where algorithms undergo random mutations and combinations between generations in an attempt to "evolve" optimal solutions, and expert systems, where computers are programmed with rules that allow them to mimic the behavior of a human expert in a specific domain, for example an autopilot system flying a plane.
Machine learning is generally split into two main categories: supervised and unsupervised learning.
This approach basically teaches machines by example.
During training for supervised learning, systems are exposed to large amounts of labelled data, for example images of handwritten figures annotated to indicate which number they correspond to. Given sufficient examples, a supervised-learning system would learn to recognize the clusters of pixels and shapes associated with each number and eventually be able to recognize handwritten numbers, able to reliably distinguish between the numbers 9 and 4 or 6 and 8.
However, training these systems typically requires huge amounts of labelled data, with some systems needing to be exposed to millions of examples to master a task.
As a result, the datasets used to train these systems can be vast, with Google's Open Images Dataset having about nine million images, its labeled video repository YouTube-8M linking to seven million labeled videos and ImageNet, one of the early databases of this kind, having more than 14 million categorized images. The size of training datasets continues to grow, with Facebook recently announcing it had compiled 3.5 billion images publicly available on Instagram, using hashtags attached to each image as labels. Using one billion of these photos to train an image-recognition system yielded record levels of accuracy -- of 85.4 percent -- on ImageNet's benchmark.
The laborious process of labeling the datasets used in training is often carried out using crowdworking services, such as Amazon Mechanical Turk, which provides access to a large pool of low-cost labor spread across the globe. For instance, ImageNet was put together over two years by nearly 50,000 people, mainly recruited through Amazon Mechanical Turk. However, Facebook's approach of using publicly available data to train systems could provide an alternative way of training systems using billion-strong datasets without the overhead of manual labeling.
In contrast, unsupervised learning tasks algorithms with identifying patterns in data, trying to spot similarities that split that data into categories.
An example might be Airbnb clustering together houses available to rent by neighborhood, or Google News grouping together stories on similar topics each day.
The algorithm isn't designed to single out specific types of data, it simply looks for data that can be grouped by its similarities, or for anomalies that stand out.
The importance of huge sets of labelled data for training machine-learning systems may diminish over time, due to the rise of semi-supervised learning.
As the name suggests, the approach mixes supervised and unsupervised learning. The technique relies upon using a small amount of labelled data and a large amount of unlabelled data to train systems. The labelled data is used to partially train a machine-learning model, and then that partially trained model is used to label the unlabelled data, a process called pseudo-labelling. The model is then trained on the resulting mix of the labelled and pseudo-labelled data.
The viability of semi-supervised learning has been boosted recently by Generative Adversarial Networks ( GANs), machine-learning systems that can use labelled data to generate completely new data, for example creating new images of Pokemon from existing images, which in turn can be used to help train a machine-learning model.
Were semi-supervised learning to become as effective as supervised learning, then access to huge amounts of computing power may end up being more important for successfully training machine-learning systems than access to large, labelled datasets.
A way to understand reinforcement learning is to think about how someone might learn to play an old school computer game for the first time, when they aren't familiar with the rules or how to control the game. While they may be a complete novice, eventually, by looking at the relationship between the buttons they press, what happens on screen and their in-game score, their performance will get better and better.
An example of reinforcement learning is Google DeepMind's Deep Q-network, which has beaten humans in a wide range of vintage video games. The system is fed pixels from each game and determines various information about the state of the game, such as the distance between objects on screen. It then considers how the state of the game and the actions it performs in game relate to the score it achieves.
Over the process of many cycles of playing the game, eventually the system builds a model of which actions will maximize the score in which circumstance, for instance, in the case of the video game Breakout, where the paddle should be moved to in order to intercept the ball.
Everything begins with training a machine-learning model, a mathematical function capable of repeatedly modifying how it operates until it can make accurate predictions when given fresh data.
Before training begins, you first have to choose which data to gather and decide which features of the data are important.
A hugely simplified example of what data features are is given in this explainer by Google, where a machine learning model is trained to recognize the difference between beer and wine, based on two features, the drinks' color and their alcoholic volume (ABV).
Each drink is labelled as a beer or a wine, and then the relevant data is collected, using a spectrometer to measure their color and hydrometer to measure their alcohol content.
An important point to note is that the data has to be balanced, in this instance to have a roughly equal number of examples of beer and wine.
The gathered data is then split, into a larger proportion for training, say about 70 percent, and a smaller proportion for evaluation, say the remaining 30 percent. This evaluation data allows the trained model to be tested to see how well it is likely to perform on real-world data.
Before training gets underway there will generally also be a data-preparation step, during which processes such as deduplication, normalization and error correction will be carried out.
The next step will be choosing an appropriate machine-learning model from the wide variety available. Each have strengths and weaknesses depending on the type of data, for example some are suited to handling images, some to text, and some to purely numerical data.
Basically, the training process involves the machine-learning model automatically tweaking how it functions until it can make accurate predictions from data, in the Google example, correctly labeling a drink as beer or wine when the model is given a drink's color and ABV.
A good way to explain the training process is to consider an example using a simple machine-learning model, known as linear regression with gradient descent. In the following example, the model is used to estimate how many ice creams will be sold based on the outside temperature.
Imagine taking past data showing ice cream sales and outside temperature, and plotting that data against each other on a scatter graph -- basically creating a scattering of discrete points.
To predict how many ice creams will be sold in future based on the outdoor temperature, you can draw a line that passes through the middle of all these points, similar to the illustration below.
Once this is done, ice cream sales can be predicted at any temperature by finding the point at which the line passes through a particular temperature and reading off the corresponding sales at that point.
Bringing it back to training a machine-learning model, in this instance training a linear regression model would involve adjusting the vertical position and slope of the line until it lies in the middle of all of the points on the scatter graph.
At each step of the training process, the vertical distance of each of these points from the line is measured. If a change in slope or position of the line results in the distance to these points increasing, then the slope or position of the line is changed in the opposite direction, and a new measurement is taken.
In this way, via many tiny adjustments to the slope and the position of the line, the line will keep moving until it eventually settles in a position which is a good fit for the distribution of all these points, as seen in the video below. Once this training process is complete, the line can be used to make accurate predictions for how temperature will affect ice cream sales, and the machine-learning model can be said to have been trained.
While training for more complex machine-learning models such as neural networks differs in several respects, it is similar in that it also uses a "gradient descent" approach, where the value of "weights" that modify input data are repeatedly tweaked until the output values produced by the model are as close as possible to what is desired.
Once training of the model is complete, the model is evaluated using the remaining data that wasn't used during training, helping to gauge its real-world performance.
To further improve performance, training parameters can be tuned. An example might be altering the extent to which the "weights" are altered at each step in the training process.
A very important group of algorithms for both supervised and unsupervised machine learning are neural networks. These underlie much of machine learning, and while simple models like linear regression used can be used to make predictions based on a small number of data features, as in the Google example with beer and wine, neural networks are useful when dealing with large sets of data with many features.
Neural networks, whose structure is loosely inspired by that of the brain, are interconnected layers of algorithms, called neurons, which feed data into each other, with the output of the preceding layer being the input of the subsequent layer.
Each layer can be thought of as recognizing different features of the overall data. For instance, consider the example of using machine learning to recognize handwritten numbers between 0 and 9. The first layer in the neural network might measure the color of the individual pixels in the image, the second layer could spot shapes, such as lines and curves, the next layer might look for larger components of the written number -- for example, the rounded loop at the base of the number 6. This carries on all the way through to the final layer, which will output the probability that a given handwritten figure is a number between 0 and 9.
See more: Special report: How to implement AI and machine learning (free PDF)
The network learns how to recognize each component of the numbers during the training process, by gradually tweaking the importance of data as it flows between the layers of the network. This is possible due to each link between layers having an attached weight, whose value can be increased or decreased to alter that link's significance. At the end of each training cycle the system will examine whether the neural network's final output is getting closer or further away from what is desired -- for instance is the network getting better or worse at identifying a handwritten number 6. To close the gap between between the actual output and desired output, the system will then work backwards through the neural network, altering the weights attached to all of these links between layers, as well as an associated value called bias. This process is called back-propagation.
Eventually this process will settle on values for these weights and biases that will allow the network to reliably perform a given task, such as recognizing handwritten numbers, and the network can be said to have "learned" how to carry out a specific task
An illustration of the structure of a neural network and how training works.
A subset of machine learning is deep learning, where neural networks are expanded into sprawling networks with a huge number of layers that are trained using massive amounts of data. It is these deep neural networks that have fueled the current leap forward in the ability of computers to carry out task like speech recognition and computer vision.
There are various types of neural networks, with different strengths and weaknesses. Recurrent neural networks are a type of neural net particularly well suited to language processing and speech recognition, while convolutional neural networks are more commonly used in image recognition. The design of neural networks is also evolving, with researchers recently devising a more efficient design for an effective type of deep neural network called long short-term memory or LSTM, allowing it to operate fast enough to be used in on-demand systems like Google Translate.
The AI technique of evolutionary algorithms is even being used to optimize neural networks, thanks to a process called neuroevolution. The approach was recently showcased by Uber AI Labs, which released papers on using genetic algorithms to train deep neural networks for reinforcement learning problems.
While machine learning is not a new technique, interest in the field has exploded in recent years.
This resurgence comes on the back of a series of breakthroughs, with deep learning setting new records for accuracy in areas such as speech and language recognition, and computer vision.
What's made these successes possible are primarily two factors, one being the vast quantities of images, speech, video and text that is accessible to researchers looking to train machine-learning systems.
But even more important is the availability of vast amounts of parallel-processing power, courtesy of modern graphics processing units (GPUs), which can be linked together into clusters to form machine-learning powerhouses.
Today anyone with an internet connection can use these clusters to train machine-learning models, via cloud services provided by firms like Amazon, Google and Microsoft.
As the use of machine-learning has taken off, so companies are now creating specialized hardware tailored to running and training machine-learning models. An example of one of these custom chips is Google's Tensor Processing Unit (TPU), the latest version of which accelerates the rate at which machine-learning models built using Google's TensorFlow software library can infer information from data, as well as the rate at which they can be trained.
These chips are not just used to train models for Google DeepMind and Google Brain, but also the models that underpin Google Translate and the image recognition in Google Photo, as well as services that allow the public to build machine learning models using Google's TensorFlow Research Cloud. The second generation of these chips was unveiled at Google's I/O conference in May last year, with an array of these new TPUs able to train a Google machine-learning model used for translation in half the time it would take an array of the top-end GPUs, and the recently announced third-generation TPUs able to accelerate training and inference even further.
As hardware becomes increasingly specialized and machine-learning software frameworks are refined, it's becoming increasingly common for ML tasks to be carried out on consumer-grade phones and computers, rather than in cloud datacenters. In the summer of 2018, Google took a step towards offering the same quality of automated translation on phones that are offline as is available online, by rolling out local neural machine translation for 59 languages to the Google Translate app for iOS and Android.
Perhaps the most famous demonstration of the efficacy of machine-learning systems was the 2016 triumph of the Google DeepMind AlphaGo AI over a human grandmaster in Go, a feat that wasn't expected until 2026. Go is an ancient Chinese game whose complexity bamboozled computers for decades. Go has about 200 moves per turn, compared to about 20 in Chess. Over the course of a game of Go, there are so many possible moves that searching through each of them in advance to identify the best play is too costly from a computational standpoint. Instead, AlphaGo was trained how to play the game by taking moves played by human experts in 30 million Go games and feeding them into deep-learning neural networks.
Training the deep-learning networks needed can take a very long time, requiring vast amounts of data to be ingested and iterated over as the system gradually refines its model in order to achieve the best outcome.
However, more recently Google refined the training process with AlphaGo Zero, a system that played "completely random" games against itself, and then learnt from the results. At last year's prestigious Neural Information Processing Systems (NIPS) conference, Google DeepMind CEO Demis Hassabis revealed AlphaGo had also mastered the games of chess and shogi.
DeepMind continue to break new ground in the field of machine learning. In July 2018, DeepMind reported that its AI agents had taught themselves how to play the 1999 multiplayer 3D first-person shooter Quake III Arena, well enough to beat teams of human players. These agents learned how to play the game using no more information than the human players, with their only input being the pixels on the screen as they tried out random actions in game, and feedback on their performance during each game.
More recently DeepMind demonstrated an AI agent capable of superhuman performance across multiple classic Atari games, an improvement over earlier approaches where each AI agent could only perform well at a single game. DeepMind researchers say these general capabilities will be important if AI research is to tackle more complex real-world domains.
Machine learning systems are used all around us, and are a cornerstone of the modern internet.
Machine-learning systems are used to recommend which product you might want to buy next on Amazon or video you want to may want to watch on Netflix.
Every Google search uses multiple machine-learning systems, to understand the language in your query through to personalizing your results, so fishing enthusiasts searching for "bass" aren't inundated with results about guitars. Similarly Gmail's spam and phishing-recognition systems use machine-learning trained models to keep your inbox clear of rogue messages.
One of the most obvious demonstrations of the power of machine learning are virtual assistants, such as Apple's Siri, Amazon's Alexa, the Google Assistant, and Microsoft Cortana.
Each relies heavily on machine learning to support their voice recognition and ability to understand natural language, as well as needing an immense corpus to draw upon to answer queries.
But beyond these very visible manifestations of machine learning, systems are starting to find a use in just about every industry. These exploitations include: computer vision for driverless cars, drones and delivery robots; speech and language recognition and synthesis for chatbots and service robots; facial recognition for surveillance in countries like China; helping radiologists to pick out tumors in x-rays, aiding researchers in spotting genetic sequences related to diseases and identifying molecules that could lead to more effective drugs in healthcare; allowing for predictive maintenance on infrastructure by analyzing IoT sensor data; underpinning the computer vision that makes the cashierless Amazon Go supermarket possible, offering reasonably accurate transcription and translation of speech for business meetings -- the list goes on and on.
Deep-learning could eventually pave the way for robots that can learn directly from humans, with researchers from Nvidia recently creating a deep-learning system designed to teach a robot to how to carry out a task, simply by observing that job being performed by a human.
As you'd expect, the choice and breadth of data used to train systems will influence the tasks they are suited to.
For example, in 2016 Rachael Tatman, a National Science Foundation Graduate Research Fellow in the Linguistics Department at the University of Washington, found that Google's speech-recognition system performed better for male voices than female ones when auto-captioning a sample of YouTube videos, a result she ascribed to 'unbalanced training sets' with a preponderance of male speakers.
As machine-learning systems move into new areas, such as aiding medical diagnosis, the possibility of systems being skewed towards offering a better service or fairer treatment to particular groups of people will likely become more of a concern.
A heavily recommended course for beginners to teach themselves the fundamentals of machine learning is this free Stanford University and Coursera lecture series by AI expert and Google Brain founder Andrew Ng.
Another highly-rated free online course, praised for both the breadth of its coverage and the quality of its teaching, is this EdX and Columbia University introduction to machine learning, although students do mention it requires a solid knowledge of math up to university level.
Technologies designed to allow developers to teach themselves about machine learning are increasingly common, from AWS' deep-learning enabled camera DeepLens to Google's Raspberry Pi-powered AIY kits.
All of the major cloud platforms -- Amazon Web Services, Microsoft Azure and Google Cloud Platform -- provide access to the hardware needed to train and run machine-learning models, with Google letting Cloud Platform users test out its Tensor Processing Units -- custom chips whose design is optimized for training and running machine-learning models.
This cloud-based infrastructure includes the data stores needed to hold the vast amounts of training data, services to prepare that data for analysis, and visualization tools to display the results clearly.
Newer services even streamline the creation of custom machine-learning models, with Google recently revealing a service that automates the creation of AI models, called Cloud AutoML. This drag-and-drop service builds custom image-recognition models and requires the user to have no machine-learning expertise, similar to Microsoft's Azure Machine Learning Studio. In a similar vein, Amazon recently unveiled new AWS offerings designed to accelerate the process of training up machine-learning models.
For data scientists, Google's Cloud ML Engine is a managed machine-learning service that allows users to train, deploy and export custom machine-learning models based either on Google's open-sourced TensorFlow ML framework or the open neural network framework Keras, and which now can be used with the Python library sci-kit learn and XGBoost.
Database admins without a background in data science can use Google's BigQueryML, a beta service that allows admins to call trained machine-learning models using SQL commands, allowing predictions to be made in database, which is simpler than exporting data to a separate machine learning and analytics environment.
For firms that don't want to build their own machine-learning models, the cloud platforms also offer AI-powered, on-demand services -- such as voice, vision, and language recognition. Microsoft Azure stands out for the breadth of on-demand services on offer, closely followed by Google Cloud Platform and then AWS.
Meanwhile IBM, alongside its more general on-demand offerings, is also attempting to sell sector-specific AI services aimed at everything from healthcare to retail, grouping these offerings together under its IBM Watson umbrella.
Early in 2018, Google expanded its machine-learning driven services to the world of advertising, releasing a suite of tools for making more effective ads, both digital and physical.
While Apple doesn't enjoy the same reputation for cutting edge speech recognition, natural language processing and computer vision as Google and Amazon, it is investing in improving its AI services, recently putting Google's former chief in charge of machine learning and AI strategy across the company, including the development of its assistant Siri and its on-demand machine learning service Core ML.
In September 2018, NVIDIA launched a combined hardware and software platform designed to be installed in datacenters that can accelerate the rate at which trained machine-learning models can carry out voice, video and image recognition, as well as other ML-related services.
The NVIDIA TensorRT Hyperscale Inference Platform uses NVIDIA Tesla T4 GPUs, which delivers up to 40x the performance of CPUs when using machine-learning models to make inferences from data, and the TensorRT software platform, which is designed to optimize the performance of trained neural networks.
There are a wide variety of software frameworks for getting started with training and running machine-learning models, typically for the programming languages Python, R, C++, Java and MATLAB.
Famous examples include Google's TensorFlow, the open-source library Keras, the Python library Scikit-learn, the deep-learning framework CAFFE and the machine-learning library Torch.
Originally posted here:
What is machine learning? Everything you need to know | ZDNet
Comments Off on What is machine learning? Everything you need to know | ZDNet