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Repeats every week until Thu Feb 13 2020 .

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By Dharma Zephyr Insight Meditation Community

Presented by Dharma Zephyr Insight Meditation Community, a meditation class series begins this January 2020 in Carson City. The 6-week class will introduce attendees to the practice of sitting, walking, and eating meditation, and teach participants how to bring mindfulness to everyday life.

Kathy and Tom will draw on what theyve learned about meditation over years of evolving practice.

Beginners and experienced meditators alike are welcome. There will be ample time for Q & A to begin or refine your practice. Core Buddhist concepts will also be explored. Some classes will include mindful movement. Preregistration is encouraged.

Go here for more information.

Tom Gray brings more than 15 years of practice to this class. He comes from a scientific perspective which is both a blessing and an obstacle; the practice has helped him to address anger and self-judgement issues. Kathy Schwerin began her practice almost 30 years ago. Everyday mindfulness has been her focus; her meditation practice has changed over the years and she is eager to share the many tools that have been helpful.

Both of them have a deep love of the Buddhist dharma because it works! There is no charge; donations gratefully accepted.

For more information or to preregister, please contact Tom@dharmazephyr.org.

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Meditation class series in Carson City begins this January - Carson Now

Tuesday, December 10, 2019 | 11:52 AM

Michael DiVittorio | Tribune-Review

Michael DiVittorio | Tribune-Review

Michael DiVittorio | Tribune-Review

Looking for something to do in Plum or Oakmont this week?

Here are some suggestions:

Meditation with a Monk is set for 6 to 7 p.m. Tuesday at the Oakmont library. Join the monks from Natrona Heights Pittsburgh Buddhist Center for an hour of relaxing meditation. The program is offered every Tuesday evening.

Somatics classes with personal trainer Tom Capriotti will be offered from 10:30 to 11:30 a.m. Thursday at the Oakmont library.

Somatics is a series of soft movements that can help alleviate lower back, neck and shoulder pain. Oakmont library programs are free. Call 412-828-9532 or go to oakmontlibrary.org for more information.

STEAM Story Time is set for 1 to 1:40 p.m. Tuesday at the Plum library. Activities are designed for 4- and 5-year-olds with their parents/caregivers and will be related to science, technology, engineering, arts and math. Registration is required.

Plum library hosts a movie matinee from 1 to 4 p.m. Thursday. This weeks feature is Murder Under the Mistletoe, a Miss Fisher mystery. Popcorn will be provided. No registration is necessary. Go to plumlibrary.org for more information on Plum library programs.

The Oaks Theater will host a special screening of National Lampoons Christmas Vacation at 7:30 p.m. Thursday at 310 Allegheny River Blvd.

Doors open at 6:30 p.m. Tickets to the all-ages event are $8. Burks BBQ in Oakmont will have dinners available at the event for $10. More information is available online at the oakstheater.com or by calling 888-718-4253.

Original post:

Happening in Plum and Oakmont this week: meditation, movies and more - TribLIVE

He who lives in harmony with himself, lives in harmony with the universe.

-Marcus Aurelius

WhatsApp deactivated.

Facebook Uninstalled.

Airplane Mode On.

For a long time, smartphone users have followed this approach to focus or simply live a stress-free life. They have cursed mobile applications for the distraction and anxiety they face in their daily lives.

But, this perspective changed drastically the moment mindfulness apps came into existence.

Mobile applications like Calm begun helping users meditate and live a peaceful live even when keeping their phone aside. A result of which is that the mindfulness meditation app market, which was valued at $959M as of 2015 and $1.21B in 2017, is expected to grow to $2.08B by 2022, with an annual average revenue growth of 11.4%.

This not only made users enjoy a peaceful life without leaving the digital world, but also proved that its a good decision to step into this market with the assistance of top app development companies.

But, before you or anyone think in this direction and invest in calm like meditation app development, it is advised to be familiar with the reason behind this popularity. Or better say, having the knowledge of key driving factors of calm like mediation app market size. A glimpse of which we will cover in the next section of the article.

In the present days, around 75% 90% of people in the United States visits doctors for stress-related issues and around 13% of kids are showing the symptoms of anxiety disorder because of stress.

Because of this, more and more people are becoming conscious about the ill effects of stress and mediation as a way to improve their mental health. Something that is giving a push to the mindfulness app development market.

With an increase in the number of smartwatches and smart screens, people are getting an opportunity to keep a real-time record of their mental health and well-being. This is again favoring the flourishing market for meditation apps.

Now while you know the reasons that ignite the zeal to develop mindfulness app like Calm or HeadSpace, lets move to the best mindfulness meditation apps in the market.

All these best mindfulness meditation apps right from Headspace to Happier, Buddhify, Insight Timer, Smiling Mind, and The Mindfulness App are doing an amazing job in the marketplace. But, the one we will focus entirely in this article is Calm application.

Introduced back in 2012 as a meditation tool, Calm has now gained a huge momentum in the marketplace as a guide for proper sleep and relaxation. The application, till date, has over 60M downloads and 80,000 new daily users. It has also received the title of the Top grossing Health and Fitness Application and the 20th Top App on the App Store. A credit of which can be given to the simple working mechanism of this application.

Whats more, the application has recently raised a funding of $88M, valuing it at $1B and collaborated with various popular products like Soundproof Phone Booth ROOM.

This altogether has made every business enthusiast interested in calm like meditation app development. It has raised a hype to getting comprehend with the technicalities of the application and build a clone with the help of top Android or iOS app development company.

Assuming that you are also one of those who wish to try their luck in this field, lets cover the technical aspects of Calm-like application here starting with taking a look into the top meditation app features.

When it comes to Calm-clone app development, the foremost feature you need to focus on is Profile Creation.

This feature, as depicted from the name, enable users to create their own profiles on the application where they can see/store all the information related to them or the activities they have participated in.

Under this category, users opt in for the best of the stories that relaxes their body and mind and help them sleep nicely.

The Calm Masterclasses feature of the app lets users avail exclusive classes from worlds best mindfulness experts across the globe.

This app feature enables users to change the scenic view that would be the background of the application.

From the morning stretch to warm up, this feature of Calm mobile app helps users come across the best ways to relax your body.

With this feature, you can set the duration of breathing exercises and exhale all of your worries without allocating a special time for meditation.

Under this category, the Calm mobile app sends different kinds of reminders to users for living a stress-free life. This includes reminders to practice mindfulness, to sleep, and to check-in the app.

These meditation app features have shown given peace of mind to users. But, what has given a new definition to calmness is the unique user experience strategy this application has worked with.

So, taking the same into consideration, lets take a sneak peek of the Calm meditation app design strategy.

Right from the icons to the typography, colors, and visual elements used in the Calm mobile application, everything gives a feeling of warmth and tranquility. A major focus has been kept on making intense topics like Stress, Anxiety, Relationships, and Self-care seem balanced and a bit more comfortable.

Calm application has also followed the concept of minimal app design thoroughly. The app has introduced the least required features and elements on the screens to give a soothing experience to users. An evidence of which is the Breathing Exercise app screen.

Calm mobile application uses the power of Push notifications to remind users about their scores, next activity schedules, and more. This is not solely helping users enjoy higher benefits of this mindfulness app, but is also making it imperative for other business enthusiasts to build a push notification strategy that boosts conversions.

The Calm app smartly categories content under different labels that eases the process of finding the right content. Also, it avails content in the voice form that lures users to pick this mindfulness meditation app over others.

The Calm-like mobile app has also invested in App Localization. Meaning, the app content is available in six different languages including English, German, and Spanish. These gives users an option to grab the content in their local languages, which increases the possibilities of acquiring more users.

With this covered, lets climb to the next level of the ladder and see what mindfulness technology stack back these functionalities.

As weve already shared in our blog on picking the best technology stack for your app, the programming languages, frameworks, and tools we consider leaves a great impact on the outcomes. While the right technology stack results in proper working of your app and enhances the results, the wrong one can ruin your apps image.

So, understanding the intensity of knowing the right tech stack, lets check for what comes under the label of mindfulness app tech stack.

Now as you know the basics technicalities, it is likely that you would be expecting discussion around the cost to develop mindfulness app like calm in the next section.

But, wait.

Before we switch to calm like meditation app development cost, it is necessary for you to be familiar with the challenges your hired app development company would face while building the app, alongside the market-related issues you would come across.

So, lets check for the challenges you might come across while building an app like Calm.

The headmost challenge your hired team app developers would face is maintaining simplicity and higher loading speed while adding ample options into the application.

When talking about app localization, there are various key challenges associated with developing multilingual apps. Mitigating those challenges and delivering the app on pre-decided timeline would again be a challenge for your hired app partners.

In the present scenario, there are various mindfulness meditation apps available in the market. However, only a few of them are backed by solid research. This has made consumers sceptical about which app to prefer and which not. And thereafter, made it challenging for business enthusiasts to win the trust of these customers and keep them hooked to their platform.

Something that is possible if you introduce the right amount of transparency into your app working.

Users, when concentrating completely on mindfulness market, are majorly not in favor of paying subscription fee for the apps. Likewise, the companies in this domain are extensively focusing on implementing effective price benchmarking to get better outcomes.

So again, higher subscription rate can be a stumbling block in the path of Entrepreneurs planning to invest in calm like meditation app development.

With this, lets wait no further and tear down the cost to build a mobile app like Calm.

The cost to make an app, as described in detail in our mobile app development cost guide, is not fixed. It varies depending on various factors such as:-

In such a scenario, the optimal way to get an exact cost of developing an app like Calm is to contact the best app experts.

Now, while this would have shared insights around how much you need to invest into the Calm-like meditation app development, lets turn towards how to earn back the money. Or simply say, lets learn about the business and revenue model of Calm mobile application.

Calm and other such mindfulness meditation apps highly rely upon subscription model. That implies, a meditation app monetization model where users pay a fixed subscription fee for unlocking the advanced features of the app on a monthly or yearly basis.

Under this label, mobile apps like Calm and HeadSpace provides users an opportunity to pay for purchasing some services/products related to meditation from the platform itself.

By embracing this Calm app business and revenue model, you would be able to generate revenue nearly equal to that of Calm application. But, to outshine the mindfulness app market, you would have to put some extra efforts.

Wondering what those extra efforts would be? What ways can you accelerate the pace of gaining the Calm-like meditation app development cost back? Lets wrap up this article covering the same.

Though the Calm app has efficiently categorized the content to deliver better search experience, a search option on the app screen can elevate the outcomes. So, look ahead to introduce a search filter option into your meditation and mindfulness app like Calm.

AR/VR technology can take users to a virtual environment which can trigger their fears and anxieties in a better manner. And this way, speed up their process of relaxing and improving their mental health.

So, discussing the integration of such cutting-edge technologies while planning Calm-like app development process is also an effective way to raise the outcomes.

While building a Calm-clone application is a profitable business, it is again a good decision to start with a MVP first. The Minimal Viable Product (MVP) will help you to enter the market with less resources, time, and budget, and improve your application depending on the changing user behavior and needs.

Since both the applications are popular in the market and offer certain value to the users, it is unwise to conclude one being better than the other without comparing. So, we advise you to go through this blog alongside that on Headspace to get an optimal answer.

When talking about developing a meditation app like Calm, the process is not as easier as it seems. You would have to focus on multiple things both from development and business front. So, we recommend you consult with top healthcare app development company for getting a relevant answer.

Some features of Calm app are free to use. But, to unlock more features and explore the world of mindfulness, you would have to pay a monthly/yearly subscription fee.

Shrikant Srivastava

VP Technology

In search for strategic sessions?.

Read the original:

The Only Resource You Need for Calm-like Meditation App Development - Appinventiv

No one had to introduce Joseph to Mary. He had known her his whole life. No dating services in those days, unless you count arranged marriages. Who knows? Perhaps a yente did the honors of officially connecting them and their families. The perfect match, she might say. Its the way things used to work, the way things still work in some places. You westerners, said a student from the Far East, fall in love and get married. We get married and fall in love.

Tradition calls Joseph a carpenter, but the word tekton (Isnt this the carpenters son?) actually can mean stone mason, which means the man had superior technical skills. Joseph the builder living in the Galilean backwoods probably earned his keep and honed his craft in cities like Capernaum and Sepphoris. It may not have earned him a kings ransom, but a man with gifted hands usually does pretty well. Any idea what plumbers make these days?

Joseph enjoyed at least one other skill not often praised in Christmas-loving circles. He paid close attention to his Bible. An engagement interrupted by a pregnancy could get a woman killed in those days. A Bible reader could easily justify it. How did Joseph, reading the same Bible, decide to divorce her quietly and so protect young Mary? What doth the Lord require of thee, but to do justice, and to love mercy, and to walk humbly? (Micah 6:8). Thats how. He did the best he could, until he knew better. What would have happened if Mary had been engaged to a mean man?

The old, odd pronoun trouble of the KJV, male-stacked genealogies, verses yanked way out of context all serve as flimsy proofs of the Bibles sexism. But as the Gospel unfolds, and through the leaves of history, Joseph takes a back seat to mother Mary. Joseph may be the patron saint of happy death, since he died in the arms of Mary and Jesus. But he could easily be the patron of all those unnoticed, forgotten holy ones. Those who keep this world spinning with their uncommon kindnesses.

The Rev. Eugene Stockstill is pastor of Ebenezer United Methodist Church and Myrtle United Methodist Church in Union County.

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MEDITATION: Joseph shows what it means to study Bible - Northeast Mississippi Daily Journal

Patrick Carr will debut songs from upcoming 2020 album at Teds on December 13. Photo by Kat Lancaster

When the Road Darkens is an apt title for singer-songwriter Patrick Carrs 2017 EP. While not a sad collection, per se, theres a soberness to his folksy storytelling and instrumentals.

An English major and self-described book nerd, Carrs songs are filled with literary references. One will recognize the nod to the green light from The Great Gatsby in Dont Let It Break Your Heart.

I adapted the title of the EP from a line found in The Lord of the Rings, Carr explains, which originally read, [f]aithless is he that says farewell when the road darkens. So far, only one person has been able to guess where the title comes from.

Though he initially organized the five tracks so they would flow musically, coincidentally, they also take listeners down a road that continues to darken around themes of hope, longing, past loves and depression. It starts with Sibylle in an Orange Hue. An introduction, lightly constructed with whimsical wind chimes, draws in listeners to an almost quiet meditation.

I find music tends to send your minds eye to a certain place or time, Carr says, so adding the wind chimes and birds in the opening was to kind of set the stage for the beginning of the EPs journey.

Inspired by a German singer-songwriter named Sibylle Baier, Carr stumbled upon a photo of her labeled Sibylle in an Orange Hue. He used some of the words from the photos title as lyrics.

In my mind, the song describes someone who is suffering from depression, and as the narrator, Im trying to convince her that everything will be alright in the end, he explains.

While Carr often involves his friends, William Glover (piano) and Sean McClain (drums), in his music, he, too, is quite a multi-instrumentalist. Mostly a self-taught guitarist playing along to Led Zeppelin records, his fingerstyle playing is heavily influenced by singer-songwriter and guitarist Nick Drake.

I pretty much spent months trying to nail that kind of fingerstyle playing and now its an integral part of my music, he notes. Ive always been interested in guitar growing up, but never really had the right reason or motivation to start playing. Once I heard Black Dog on my dads copy of the fourth Led Zeppelin record, I really wanted to learn so I could play that song.

Carr also plays bass, organ, ukulele, mandolin, glockenspiel (similar to a xylophone), and an Indian drone instrument called a shruti box. According to Carr, bass isnt hard when starting with a foundation in guitar, but it needs to be approached differently than the guitar.

Paying attention to bassists like Carol Kaye from the Wrecking Crew kind of gave me that light bulb moment, he continues. I started playing ukulele when I was a junior in high school after a friend brought his along on a field trip. Mandolin is a new instrument for me and Ive really only played it seriously for about two or three years now. I just kind of love stringed instruments, so I end up collecting and playing them. . . . but Im not the best mandolin player so sitting in a bluegrass jam would be catastrophic.

Carrs upcoming show at Teds will include performances of tunes from When the Road Darkens. He will play a few new songs as well, indicative of his influences from Nick Drake to Ryley Walker to Bon Iver. Lyrically, they are even more introspective, often dealing with feeling lost and trying to navigate life. What remains consistent is his penchant for literature, as heard in The Jaws of Hell. I took the title from a Radiohead lyric and the song makes a very loose connection to Dantes Inferno, Carr says.

In October, Carr recorded a few tracks featuring a flute and saxophone player, and Glover back on piano. The plan is to have two new records in 2020: an instrumental EP called Vox Humana and a full-length album.

So far I havent settled on a title for the full-length record yet, he notes. Ive got a few different titles that Im kicking around right now, but Im kind of waiting for the entire record to be finished before I decide on the title that feels right.

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QUIET MEDITATIONS: Patrick Carr set to play solo show at Ted's ILM's Alternative Weekly Voice - encore Online

$SPY

You may have heard that trading meditation can help you trade better, but it is not for everyone, though I have yet to hear a case where it has not helped after giving it an honest effort

Here, I will introduce you meditation for traders and hopefully remove some of the roadblocks that have prevented you from giving it a try in the past.

You will learn why it works, how it can benefit and how to meditate properly.

It is something that I believe should be part of what some call holistic trading, where one is not just looking for entries and exits, but consider all aspects of your life and how they can improve your trading.

Trading better usually means that your quality of life will also improve, and the 2 will continually build upon each other.

You may think that doing meditation means that you have to join a religion or live in a commune or something, no.

Although many meditation practices do have religious roots, it is not a requirement that you join any type of group to get the benefits.

It is similar to trading, where you are free to learn from different teachers and take what works and leave what does not.

Another misconception is that it is time consuming, complicated or mystical. It can really be as simple or complex as you make it.

Just like in trading do what works for you.

Meditation works by allowing us to control the state of our minds.

We can measure these states by the frequency of the electromagnetic waves that our brains give off. The measurement process is called electroencephalography or EEG, for short.

There are different tools that you can use at home to measure your brainwaves.

If you are not familiar with the different types of brain waves, below is a short summary.

Mediation is associated with the theta and delta states, but I have included the others so you understand the bigger picture.

The human brain does not only operate in one state at a time, but in fact gives off brain waves of these frequencies all the time.

When I talk about being in these states however, I am referring to the dominant frequency.

Gamma: When your brain is giving off frequencies of greater than 30 Hz, you are in gamma state. This is the state that is most conducive to active learning and information retention.

It is also probably the least studied state. If you need to learn something, consider trying to increase your brain activity before you sit down to study.

This is the state that monks can frequently achieve after years of practice

Beta: This is when we are giving off frequencies between about 12 30 Hz, we are in an alert state and doing normal daily functions like analyzing and planning.

Alpha: This refers to the brain waves that are in the frequency between about 8-12 Hz. This frequency range is associated with conscious reflection and a relaxed state of being. This is often when we are most creative.

Theta: Now we get into where meditation starts. When the brain is giving off waves in the 4-8 Hz range.

It is a state of deep relaxation and awareness. In this state, you have an increased ability to visualize things and solve complex problems creatively.

Delta: When you go down past theta, you are in delta, which is less than 4 Hz. When the brain is in this state, you are often in deep sleep, although some long-time practitioners of meditation can reach this state while awake. This state is associated with healing.

The Big Q: How do you know what state you are in?

The Big A: There are many devices of varying complexity that will measure your brain wave activity or biofeedback devices that will measure your stress level.

Look around and see what suits your needs and budget.

There are many good reasons to practice meditation. In trading terms, it can lead to better concentration, calm under pressure and improved overall performance.

The benefits extend way beyond trading however. You will probably find that you are more relaxed in your everyday life and are able to cope with situations that may have overwhelmed you in the past.

According to a Harvard study, meditation has even been shown toward off disease.

Now that you know how it can benefit you and how it works, take a look at how to meditate properly, as follows:

A Basic Trading Meditation

If you just want something to get started, here is simplest form of meditation that you can do.

Just do it every day for 5 days, see how you feel.

You may have heard of this type of meditation before because it is very popular.

The Key reason thatTranscendental Meditationhas become so popular is because of its simplicity and the fact that it is does not tie itself to any religion or lifestyle.

For people who do not like to sit still, Qi Gong may be a good alternative. It isnt meditation in the more well-known sense, where you sit and close your eyes, but it does provide many of the same benefits.

The idea behind this Taoist practice is that you are cultivating and distributing your bodys energy. You move and breathe in ways that help you become more centered and energized. I do these exercises occasionally and I feel great afterwards.

If you dig deep enough, you will find that almost all of the major world religions practice some form of meditation. Even if you do not believe in the religious part, you can still learn a lot by studying these practices.

Buddhism is the religion most frequently associated with meditation.

Keep in mind that you may have to do some searching to find what works best for you, so do not get discouraged if you do not feel any benefits right away.

Just enjoy the trading meditation process and understand that it is a lifelong endeavor.

Have a terrific week.

benefits, breathe, meditate, meditation, relax, SPY, stress, traders, trading

Paul A. Ebeling, polymath, excels in diverse fields of knowledge. Pattern Recognition Analyst in Equities, Commodities and Foreign Exchange and author of The Red Roadmasters Technical Report on the US Major Market Indices, a highly regarded, weekly financial market letter, he is also a philosopher, issuing insights on a wide range of subjects to a following of over 250,000 cohorts. An international audience of opinion makers, business leaders, and global organizations recognizes Ebeling as an expert.

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Get Started With Trading Meditation - Live Trading News

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Is there a person in your life who alwayshas a smoothie in-hand and is ready for a5amSoulCycleclass at a moments notice? This is probably the gift list for them.

If Gwyneth PaltrowsGoop gift guide wasnt quite goopy enough for you this holiday season, we've rounded up some of our favorite wellness gifts for the early rising, chia-loving, exercise-class hopper.

Chocolate Meditation Collection

Chocolate Meditation Collection with Lilly Pulitzer Terri Cashmere scarf (Sara Feigin)

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This is the ideal gift for a friend who can tell you what every single type of crystal does. The Chocolate Meditation Collection gives people truffles chosen for their properties along with a crystal pairing and affirmation cards. Simply grab the chocolate that sounds most appealing (there are plenty of inventive flavors, like curry or olive) and its corresponding crystal, then recite the chosen mantra that accompanies both chocolate and crystal.

Bawdy Beauty Butt Masks

Bawdy Beauty Butt Masks, $8 (Sara Feigin)

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Want to revitalize your body after a particularly intense spin class? Try Bawdy Beautys butt sheet masks. They tone, detoxify and rejuvenate your skin, with sheets for each cheek with cheeky sayings on them. Theyre also vegan, plant-based and clean.

Bawdy Beauty encourages users to post 'belfies' in the mask on Instagram. Simply put one on and either wander around your house (although you could scare a flatmate) or relax while watching Netflix (on your stomach, of course).

Yoga Club

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These days, there are subscriptions for everything, whether youre shopping for a discerning cook or a dog-lover. But Yoga Box is for your pal who sweats at Y7 religiously. Like other fashion boxes, either you or the gift recipient can fill out a long survey with what theyre looking for in athleisure, whether its mesh cut-outs or colorful prints. The box then arrives monthly.

Toast Full Spectrum Hemp Extract

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If you have a pal who wont stop chugging CBD soda, give them the gift of Toast. The brand offers also offers CBD pre-rolls with no tobacco- but if thats not their vibe, give them the oil, which they can add to their wellness smoothie in the morning. Its vegan, gluten free and sugar free so it should be ideal for any and all wellness fanatics.

DeoDoc kit

DeoDoc kit, $55 (Sara Feigin)

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What's goopier than an intimate grooming kit? We imagine this is would be GP approved (although it's no crystal). Deodoc's particular brand of 'intimate skincare' was developed by a doctor and is perfect for your friend who's a modern-day Samantha from Sex and the City - i.e. can't stop gossiping about men at brunch but loves hitting hot yoga. Get them the butt mask, too.

Souljourn Yoga

Souljourn'sretreats start at $400 (Souljourn)

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If you want to give a gift thats a bit more meaningful that most to your most wellness obsessed friend, Souljourn Yoga is the way to go. Its a non-profit that hosts immersive retreats all around the world to raise money for girls education in developing countries. In 2020, they already have retreats planned to Cape Town, Sri Lanka, Peru, Rwanda, Tibet and Morocco - and you could always get yourself a ticket, as well - after all, it is a vacation for a good cause.

DosistDissolvable Tablets

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Dosist is all the rage in Los Angeles - but if you can't make it to LA to grab one of their pens, try the CBD-infused tablets for a post-workout buzz.

SPRI

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If you want to purchase something useful for the wellness aficionado in your life that lets them pretend theyre at their favorite boutique fitness class, try SPRI. The products are used at fitness classes all over, including New Yorks intense workout at Switch Playground. The best part? The weights come in different colors, so your pal won't mind leaving them lying around her studio apartment if she doesn't have space for an in-home gym.

Mineral Sousa

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For your friend who loves CrossFit and is always chugging a Coconut Water as part of recovery, gift them this luxurious CBD oil meant to help with post-workout inflammation.

Simris Algae Pills

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Get your favorite wellness friend these on-trend algae pills, made for athletes, mothers or fitness aficionados. The blue bottles look chic on a work desk and the pills themselves contain Omega-3, which is particularly helpful for your vegan pals. They're vegan, gluten-free, non-toxic and '100 percent ocean friendly,' so say goodbye to actual fish oil forever.

LARQ Self-Cleaning Water Bottle

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If you have a friend who refuses to put down their S'well water bottle it's time to upgrade them. LARQ's bottles are sleek and chic - but even better, they purify your water while you drink, neutralizing all harmful bacteria that could be lurking.

The White Company Sleep Kit

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Once you've worked out, loaded up on CBD and are ready for bed, it's time for natural remedies. The White Company offers up a Sleep Well Gift Set that's ideal for your friend who's too wellness obsessed to even consider taking Melatonin. It comes with lotion, a candle and even sleep spray.

Read this article:

Christmas gift ideas for wellness lovers, from chocolate crystal meditation to yoga retreats - Evening Standard

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4. To organize for a new product launch and inventory in advance.

5. To identify potential business partners, acquisition, targets and buyers.

6. To design capital investment strategies depending on forecasted high potential segments.

Market Segmentation:

Meditation Cushion Market Segment By Top Competitors

Satori Wholesale Trevida Peace Yoga Seat Of Your Soul Waterglider International Bean Products

Meditation Cushion Market Segment ByTypes, Estimates and Forecast, 2020-2029

Kapok Fill Buckwheat Fill Memory Foam Fill Others

Meditation Cushion Market Segment ByApplications, Estimates and Forecast, 2020-2029

Commercial Household

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Highlights of the following key factors:

Business overview: An overall information of the organizations operations and business divisions and Background.

Company history: Evolution of key events associated with the organization.

Business strategy: Summarization of the organizations business strategy by Analysts.

Major products and services: A list of major brands, products and services of the organization.

Key competitors: A checklist of main competitors to the company.

Company locations and subsidiaries: A list and contact details of key locations and subsidiaries of the organization.

Detailed financial ratios for the past ten years: The latest financial ratios derived from the annual financial statements published by the organization with 10 years of history.

SWOT Analysis: A complete analysis of the organizations stability, flaws, opportunities, and obstacles.

Moreover, other factors that contribute toward the increase in growth of the Meditation Cushion market include sympathetic government initiatives related to the use of Meditation Cushion. On the contrary, high growth potential in emerging economies is expected to create lucrative opportunities for the market during the forecast period, (2020-2029)

Major Points Covered in Table of Contents:

1. Global Meditation Cushion Market Synopsis

2. Global Meditation Cushion Market Status and Development

3. Global Meditation Cushion Market Analysis by Manufacturers

4. Global Meditation Cushion Supply (Production), Consumption, Export, Import by Region (2020-2029)

5. Meditation Cushion Production, Revenue (Value), Price Trend by Type

6. Global Meditation Cushion Market Analysis by Application

7. Global Meditation Cushion Manufacturers Profiles/Analysis

8. Meditation Cushion Manufacturing Cost Analysis, Industry Chain, Upstream, and Downstream Customers Analysis

9. Regional and Industry Investment Opportunities & Challenges, Hazards and Affecting Factors

10. Marketing Strategy Analysis, Distributors/Traders

11. Global Meditation Cushion Market Forecast (2020-2029)

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Meditation Cushion Market Upcoming Business Opportunities with New Innovative Solutions - Electronics Reports

Digital Human Brain Covered with Networks

Artificial intelligence is advancing rapidly. In a few decades machines will achieve superintelligence and become self-improving. Soon after that happens we will launch a thousand ships into space. These probes will land on distant planets, moons, asteroids, and comets. Using AI and terabytes of code, they will then nanoassemble local particles into living organisms. Each probe will, in fact, contain the information needed to create an entire ecosystem. Thanks to AI and advanced biotechnology, the species in each place will be tailored to their particular plot of rock. People will thrive in low temperatures, dim light, high radiation, and weak gravity. Humanity will become an incredibly elastic concept. In time our distant progeny will build megastructures that surround stars and capture most of their energy. Then the power of entire galaxies will be harnessed. Then life and AIlong a common entity by this pointwill construct a galaxy-sized computer. It will take a mind that large about a hundred-thousand years to have a thought. But those thoughts will pierce the veil of reality. They will grasp things as they really are. All will be one. This is our destiny.

Then again, maybe not.

There are, of course, innumerable reasons to reject this fantastic tale out of hand. Heres a quick and dirty one built around Copernicuss discovery that we are not the center of the universe. Most times, places, people, and things are average. But if sentient beings from Earth are destined to spend eons multiplying and spreading across the heavens, then those of us alive today are special. We are among the very few of our kind to live in our cosmic infancy, confined in our planetary cradle. Because we probably are not special, we probably are not at an extreme tip of the human timeline; were likely somewhere in the broad middle. Perhaps a hundred-billion modern humans have existed, across a span of around 50,000 years. To claim in the teeth of these figures that our species is on the cusp of spending millions of years spreading trillions of individuals across this galaxy and others, you must engage in some wishful thinking. You must embrace the notion that we today are, in a sense, back at the center of the universe.

It is in any case more fashionable to speculate about imminent catastrophes. Technology again looms large. In the gray goo scenario, runaway self-replicating nanobots consume all of the Earths biomass. Thinking along similar lines, philosopher Nick Bostrom imagines an AI-enhanced paperclip machine that, ruthlessly following its prime directive to make paperclips, liquidates mankind and converts the planet into a giant paperclip mill. Elon Musk, when he discusses this hypothetical, replaces paperclips with strawberries, so that he can worry about strawberry fields forever. What Bostrom and Musk are driving at is the fear that an advanced AI being will not share our values. We might accidently give it a bad aim (e.g., paperclips at all costs). Or it might start setting its own aims. As Stephen Hawking noted shortly before his death, a machine that sees your intelligence the way you see a snails might decide it has no need for you. Instead of using AI to colonize distant planets, we will use it to destroy ourselves.

When someone mentions AI these days, she is usually referring to deep neural networks. Such networks are far from the only form of AI, but they have been the source of most of the recent successes in the field. A deep neural network can recognize a complex pattern without relying on a large body of pre-set rules. It does this with algorithms that loosely mimic how a human brain tunes neural pathways.

The neurons, or units, in a deep neural network are layered. The first layer is an input layer that breaks incoming data into pieces. In a network that looks at black-and-white images, for instance, each of the first layers units might link to a single pixel. Each input unit in this network will translate its pixels grayscale brightness into a number. It might turn a white pixel into zero, a black pixel into one, and a gray pixel into some fraction in between. These numbers will then pass to the next layer of units. Each of the units there will generate a weighted sum of the values coming in from several of the previous layers units. The next layer will do the same thing to that second layer, and so on through many layers more. The deeper the layer, the more pixels accounted for in each weighted sum.

An early-layer unit will produce a high weighted sumit will fire, like a neuron doesfor a pattern as simple as a black pixel above a white pixel. A middle-layer unit will fire only when given a more complex pattern, like a line or a curve. An end-layer unit will fire only when the patternor, rather, the weighted sums of many other weighted sumspresented to it resembles a chair or a bonfire or a giraffe. At the end of the network is an output layer. If one of the units in this layer reliably fires only when the network has been fed an image with a giraffe in it, the network can be said to recognize giraffes.

A deep neural network is not born recognizing objects. The network just described would have to learn from pre-labeled examples. At first the network would produce random outputs. Each time the network did this, however, the correct answers for the labeled image would be run backward through the network. An algorithm would be used, in other words, to move the networks unit weighting functions closer to what they would need to be to recognize a given object. The more samples a network is fed, the more finely tuned and accurate it becomes.

Some deep neural networks do not need spoon-fed examples. Say you want a program equipped with such networks to play chess. Give it the rules of the game, instruct it to seek points, and tell it that a checkmate is worth a hundred points. Then have it use a Monte Carlo method to randomly simulate games. Through trial and error, the program will stumble on moves that lead to a checkmate, and then on moves that lead to moves that lead to a checkmate, and so on. Over time the program will assign value to moves that simply tend to lead toward a checkmate. It will do this by constantly adjusting its networks unit weighting functions; it will just use points instead of correctly labeled images. Once the networks are trained, the program can win discrete contests in much the way it learned to play in the first place. At each of its turns, the program will simulate games for each potential move it is considering. It will then choose the move that does best in the simulations. Thanks to constant fine-tuning, even these in-game simulations will get better and better.

There is a chess program that operates more or less this way. It is called AlphaZero, and at present it is the best chess player on the planet. Unlike other chess supercomputers, it has never seen a game between humans. It learned to play by spending just a few hours simulating moves with itself. In 2017 it played a hundred games against Stockfish 8, one of the best chess programs to that point. Stockfish8 examined 70million moves per second. AlphaZero examined only 80,000. AlphaZero won 28 games, drew 72, and lost zero. It sometimes made baffling moves (to humans) that turned out to be masterstrokes. AlphaZero is not just a chess genius; it is an alien chess genius.

AlphaZero is at the cutting edge of AI, and it is very impressive. But its success is not a sign that AI will take us to the starsor enslave usany time soon. In Artificial Intelligence: A Guide For Thinking Humans, computer scientist Melanie Mitchell makes the case for AI sobriety. AI currently excels, she notes, only when there are clear rules, straightforward reward functions (for example, rewards for points gained or for winning), and relatively few possible actions (moves). Take IBMs Watson program. In 2011 it crushed the best human competitors on the quiz show Jeopardy!, leading IBM executives to declare that its successors would soon be making legal arguments and medical diagnoses. It has not worked out that way. Real-world questions and answers in real-world domains, Mitchell explains, have neither the simple short structure of Jeopardy! clues nor their well-defined responses.

Even in the narrow domains that most suit it, AI is brittle. A program that is a chess grandmaster cannot compete on a board with a slightly different configuration of squares or pieces. Unlike humans, Mitchell observes, none of these programs can transfer anything it has learned about one game to help it learn a different game. Because the programs cannot generalize or abstract from what they know, they can function only within the exact parameters in which they have been trained.

A related point is that current AI does not understand even basic aspects of how the world works. Consider this sentence: The city council refused the demonstrators a permit because they feared violence. Who feared violence, the city council or the demonstrators? Using what she knows about bureaucrats, protestors, and riots, a human can spot at once that the fear resides in the city council. When AI-driven language-processing programs are asked this kind of question, however, their responses are little better than random guesses. When AI cant determine what it refers to in a sentence, Mitchell writes, quoting computer scientist Oren Etzioni, its hard to believe that it will take over the world.

And it is not accurate to say, as many journalists do, that a program like AlphaZero learns by itself. Humans must painstakingly decide how many layers a network should have, how much incoming data should link to each input unit, how fast data should aggregate as it passes through the layers, how much each unit weighting function should change in response to feedback, and much else. These settings and designs, adds Mitchell, must typically be decided anew for each task a network is trained on. It is hard to see nefarious unsupervised AI on the horizon.

The doom camp (AI will murder us) and the rapture camp (it will take us into the mind of God) share a common premise. Both groups extrapolate from past trends of exponential progress. Moores lawwhich is not really a law, but an observationsays that the number of transistors we can fit on a computer chip doubles every two years or so. This enables computer processing speeds to increase at an exponential rate. The futurist Ray Kurzweil asserts that this trend of accelerating improvement stretches back to the emergence of life, the appearance of Eukaryotic cells, and the Cambrian Explosion. Looking forward, Kurzweil sees an AI singularitythe rise of self-improving machine superintelligenceon the trendline around 2045.

The political scientist Philip Tetlock has looked closely at whether experts are any good at predicting the future. The short answer is that theyre terrible at it. But theyre not hopeless. Borrowing an analogy from Isaiah Berlin, Tetlock divides thinkers into hedgehogs and foxes. A hedgehog knows one big thing, whereas a fox knows many small things. A hedgehog tries to fit what he sees into a sweeping theory. A fox is skeptical of such theories. He looks for facts that will show he is wrong. A hedgehog gives answers and says moreover a lot. A fox asks questions and says however a lot. Tetlock has found that foxes are better forecasters than hedgehogs. The more distant the subject of the prediction, the more the hedgehogs performance lags.

Using a theory of exponential growth to predict an impending AI singularity is classic hedgehog thinking. It is a bit like basing a prediction about human extinction on nothing more than the Copernican principle. Kurzweils vision of the future is clever and provocative, but it is also hollow. It is almost as if huge obstacles to general AI will soon be overcome because the theory says so, rather than because the scientists on the ground will perform the necessary miracles. Gordon Moore himself acknowledges that his law will not hold much longer. (Quantum computers might pick up the baton. Well see.) Regardless, increased processing capacity might be just a small piece of whats needed for the next big leaps in machine thinking.

When at Thanksgiving dinner you see Aunt Jane sigh after Uncle Bob tells a blue joke, you can form an understanding of what Jane thinks about what Bob thinks. For that matter, you get the joke, and you can imagine analogous jokes that would also annoy Jane. You can infer that your cousin Mary, who normally likes such jokes but is not laughing now, is probably still angry at Bob for spilling the gravy earlier. You know that although you cant see Bobs feet, they exist, under the table. No deep neural network can do any of this, and its not at all clear that more layers or faster chips or larger training sets will close the gap. We probably need further advances that we have only just begun to contemplate. Enabling machines to form humanlike conceptual abstractions, Mitchell declares, is still an almost completely unsolved problem.

There has been some concern lately about the demise of the corporate laboratory. Mitchell gives the impression that, at least in the technology sector, the corporate basic-research division is alive and well. Over the course of her narrative, labs at Google, Microsoft, Facebook, and Uber make major breakthroughs in computer image recognition, decision making, and translation. In 2013, for example, researchers at Google trained a network to create vectors among a vast array of words. A vector set of this sort enables a language-processing program to define and use a word based on the other words with which it tends to appear. The researchers put their vector set online for public use. Google is in some ways the protagonist of Mitchells story. It is now an applied AI company, in Mitchells words, that has placed machine thinking at the center of diverse products, services, and blue-sky research.

Google has hired Ray Kurzweil, a move that might be taken as an implicit endorsement of his views. It is pleasing to think that many Google engineers earnestly want to bring on the singularity. The grand theory may be illusory, but the treasures produced in pursuit of it will be real.

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Doubting The AI Mystics: Dramatic Predictions About AI Obscure Its Concrete Benefits - Forbes

Image credit: Depositphotos

This article is part ofDemystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.

Last week, Lee Se-dol, the South Korean Go champion who lost in a historical matchup against DeepMinds artificial intelligence algorithm AlphaGo in 2016, declared his retirement from professional play.

With the debut of AI in Go games, Ive realized that Im not at the top even if I become the number one through frantic efforts, Lee told theYonhap news agency. Even if I become the number one, there is an entity that cannot be defeated.

Predictably, Se-dols comments quickly made the rounds across prominent tech publications, some of them using sensational headlines with AI dominance themes.

Since the dawn of AI, games have been one of the main benchmarks to evaluate the efficiency of algorithms. And thanks to advances in deep learning and reinforcement learning, AI researchers are creating programs that can master very complicated games and beat the most seasoned players across the world. Uninformed analysts have been picking up on these successes to suggest that AI is becoming smarter than humans.

But at the same time, contemporary AI fails miserably at some of the most basic that every human can perform.

This begs the question, does mastering a game prove anything? And if not, how can you measure the level of intelligence of an AI system?

Take the following example. In the picture below, youre presented with three problems and their solution. Theres also a fourth task that hasnt been solved. Can you guess the solution?

Youre probably going to think that its very easy. Youll also be able to solve different variations of the same problem with multiple walls, and multiple lines, and lines of different colors, just by seeing these three examples. But currently, theres no AI system, including the ones being developed at the most prestigious research labs, that can learn to solve such a problem with so few examples.

The above example is from The Measure of Intelligence, a paper by Franois Chollet, the creator of Keras deep learning library. Chollet published this paper a few weeks before Le-sedol declared his retirement. In it, he provided many important guidelines on understanding and measuring intelligence.

Ironically, Chollets paper did not receive a fraction of the attention it needs. Unfortunately, the media is more interested in covering exciting AI news that gets more clicks. The 62-page paper contains a lot of invaluable information and is a must-read for anyone who wants to understand the state of AI beyond the hype and sensation.

But I will do my best to summarize the key recommendations Chollet makes on measuring AI systems and comparing their performance to that of human intelligence.

The contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks, such as board games and video games, Chollet writes, adding that solely measuring skill at any given task falls short of measuring intelligence.

In fact, the obsession with optimizing AI algorithms for specific tasks has entrenched the community in narrow AI. As a result, work in AI has drifted away from the original vision of developing thinking machines that possess intelligence comparable to that of humans.

Although we are able to engineer systems that perform extremely well on specific tasks, they have still stark limitations, being brittle, data-hungry, unable to make sense of situations that deviate slightly from their training data or the assumptions of their creators, and unable to repurpose themselves to deal with novel tasks without significant involvement from human researchers, Chollet notes in the paper.

Chollets observations are in line with those made by other scientists on the limitations and challenges of deep learning systems. These limitations manifest themselves in many ways:

Heres an example: OpenAIs Dota-playing neural networks needed 45,000 years worth of gameplay to reach a professional level. The AI is also limited in the number of characters it can play, and the slightest change to the game rules will result in a sudden drop in its performance.

The same can be seen in other fields, such as self-driving cars. Despite millions of hours of road experience, the AI algorithms that power autonomous vehicles can make stupid mistakes, such as crashing into lane dividers or parked firetrucks.

One of the key challenges that the AI community has struggled with is defining intelligence. Scientists have debated for decades on providing a clear definition that allows us to evaluate AI systems and determine what is intelligent or not.

Chollet borrows the definition by DeepMind cofounder Shane Legg and AI scientist Marcus Hutter: Intelligence measures an agents ability to achieve goals in a wide range of environments.

Key here is achieve goals and wide range of environments. Most current AI systems are pretty good at the first part, which is to achieve very specific goals, but bad at doing so in a wide range of environments. For instance, an AI system that can detect and classify objects in images will not be able to perform some other related task, such as drawing images of objects.

Chollet then examines the two dominant approaches in creating intelligence systems: symbolic AI and machine learning.

Early generations of AI research focused on symbolic AI, which involves creating an explicit representation of knowledge and behavior in computer programs. This approach requires human engineers to meticulously write the rules that define the behavior of an AI agent.

It was then widely accepted within the AI community that the problem of intelligence would be solved if only we could encode human skills into formal rules and encode human knowledge into explicit databases, Chollet observes.

But rather than being intelligent by themselves, these symbolic AI systems manifest the intelligence of their creators in creating complicated programs that can solve specific tasks.

The second approach, machine learning systems, is based on providing the AI model with data from the problem space and letting it develop its own behavior. The most successful machine learning structure so far is artificial neural networks, which are complex mathematical functions that can create complex mappings between inputs and outputs.

For instance, instead of manually coding the rules for detecting cancer in x-ray slides, you feed a neural network with many slides annotated with their outcomes, a process called training. The AI examines the data and develops a mathematical model that represents the common traits of cancer patterns. It can then process new slides and outputs how likely it is that the patients have cancer.

Advances in neural networks and deep learning have enabled AI scientists to tackle many tasks that were previously very difficult or impossible with classic AI, such as natural language processing, computer vision and speech recognition.

Neural networkbased models, also known as connectionist AI, are named after their biological counterparts. They are based on the idea that the mind is a blank slate (tabula rasa) that turns experience (data) into behavior. Therefore, the general trend in deep learning has become to solve problems by creating bigger neural networks and providing them with more training data to improve their accuracy.

Chollet rejects both approaches because none of them has been able to create generalized AI that is flexible and fluid like the human mind.

We see the world through the lens of the tools we are most familiar with. Today, it is increasingly apparent that both of these views of the nature of human intelligenceeither a collection of special-purpose programs or a general-purpose Tabula Rasaare likely incorrect, he writes.

Truly intelligent systems should be able to develop higher-level skills that can span across many tasks. For instance, an AI program that masters Quake 3 should be able to play other first-person shooter games at a decent level. Unfortunately, the best that current AI systems achieve is local generalization, a limited maneuver room within their own narrow domain.

In his paper, Chollet argues that the generalization or generalization power for any AI system is its ability to handle situations (or tasks) that differ from previously encountered situations.

Interestingly, this is a missing component of both symbolic and connectionist AI. The former requires engineers to explicitly define its behavioral boundary and the latter requires examples that outline its problem-solving domain.

Chollet also goes further and speaks of developer-aware generalization, which is the ability of an AI system to handle situations that neither the system nor the developer of the system have encountered before.

This is the kind of flexibility you would expect from a robo-butler that could perform various chores inside a home without having explicit instructions or training data on them. An example is Steve Wozniaks famous coffee test, in which a robot would enter a random house and make coffee without knowing in advance the layout of the home or the appliances it contains.

Elsewhere in the paper, Chollet makes it clear that AI systems that cheat their way toward their goal by leveraging priors (rules) and experience (data) are not intelligent. For instance, consider Stockfish, the best rule-base chess-playing program. Stockfish, an open-source project, is the result of contributions from thousands of developers who have created and fine-tuned tens of thousands of rules. A neural networkbased example is AlphaZero, the multi-purpose AI that has conquered several board games by playing them millions of times against itself.

Both systems have been optimized to perform a specific task by making use of resources that are beyond the capacity of the human mind. The brightest human cant memorize tens of thousands of chess rules. Likewise, no human can play millions of chess games in a lifetime.

Solving any given task with beyond-human level performance by leveraging either unlimited priors or unlimited data does not bring us any closer to broad AI or general AI, whether the task is chess, football, or any e-sport, Chollet notes.

This is why its totally wrong to compare Deep Blue, Alpha Zero, AlphaStar or any other game-playing AI with human intelligence.

Likewise, other AI models, such as Aristo, the program that can pass an eighth-grade science test, does not possess the same knowledge as a middle school student. It owes its supposed scientific abilities to the huge corpora of knowledge it was trained on, not its understanding of the world of science.

(Note: Some AI researchers, such as computer scientist Rich Sutton, believe that the true direction for artificial intelligence research should be methods that can scale with the availability of data and compute resources.)

In the paper, Chollet presents the Abstraction Reasoning Corpus (ARC), a dataset intended to evaluate the efficiency of AI systems and compare their performance with that of human intelligence. ARC is a set of problem-solving tasks that tailored for both AI and humans.

One of the key ideas behind ARC is to level the playing ground between humans and AI. It is designed so that humans cant take advantage of their vast background knowledge of the world to outmaneuver the AI. For instance, it doesnt involve language-related problems, which AI systems have historically struggled with.

On the other hand, its also designed in a way that prevents the AI (and its developers) from cheating their way to success. The system does not provide access to vast amounts of training data. As in the example shown at the beginning of this article, each concept is presented with a handful of examples.

The AI developers must build a system that can handle various concepts such as object cohesion, object persistence, and object influence. The AI system must also learn to perform tasks such as scaling, drawing, connecting points, rotating and translating.

Also, the test dataset, the problems that are meant to evaluate the intelligence of the developed system, are designed in a way that prevents developers from solving the tasks in advance and hard-coding their solution in the program. Optimizing for evaluation sets is a popular cheating method in data science and machine learning competitions.

According to Chollet, ARC only assesses a general form of fluid intelligence, with a focus on reasoning and abstraction. This means that the test favors program synthesis, the subfield of AI that involves generating programs that satisfy high-level specifications. This approach is in contrast with current trends in AI, which are inclined toward creating programs that are optimized for a limited set of tasks (e.g., playing a single game).

In his experiments with ARC, Chollet has found that humans can fully solve ARC tests. But current AI systems struggle with the same tasks. To the best of our knowledge, ARC does not appear to be approachable by any existing machine learning technique (including Deep Learning), due to its focus on broad generalization and few-shot learning, Chollet notes.

While ARC is a work in progress, it can become a promising benchmark to test the level of progress toward human-level AI. We posit that the existence of a human-level ARC solver would represent the ability to program an AI from demonstrations alone (only requiring a handful of demonstrations to specify a complex task) to do a wide range of human-relatable tasks of a kind that would normally require human-level, human-like fluid intelligence, Chollet observes.

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Artificial intelligence: How to measure the I in AI - TechTalks