Category Archives: Analytics

The Current State of Intelligent Systems

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Aiva, an AI-powered music composer, suggests algorithms are capable of remarkable creativity. Google Translate’s invention of its own interlingua—the ability to translate between two languages it has not yet been trained to interpret—may lead some to fear the machines are on the brink of autonomy.

So much of what is written about the future of AI amounts to broad philosophical statements, often about the nature of humanity, knowledge and life itself. We asked Sheldon Pacotti, Senior Solution Architect at frog Austin, to unpack some of this complexity for us. Here he shares his view on the current state of AI in product design, its common misconceptions and a look at where we’re going next.

Sheldon Pacotti, Senior Solution Architect, frog

Why does AI spark such philosophical debate?   

What’s unfortunate—but fun—about AI is that it’s in fact deeply entwined with the field of philosophy. Analytical philosophy, from Hume to 20th century figures like Donald Davidson, has had a direct impact on how truth is represented in “symbolist” AI systems, for instance. As a computer scientist, I would say that W. V. Quine’s theory of “observation sentences” and Jerry Fodor’s Language of Thought both read like software architecture.

It’s hard to imagine creating intelligent human-machine interfaces without considering epistemology, phenomenology and other -ologies developed by abstract thinkers. Complicating things further, a half-dozen other fields, including neuroscience, mathematics and cognitive science, are also core to the advance of AI. This situation makes it much more difficult to predict when we will devise the “right” theories of intelligence, and in what order these theories might appear. To build a thinking machine, you need a good theory of thought, perception and reasoning.

In design, through the practice of cultivating empathy, we routinely apply this kind of thinking to users. What is changing is that we must now consider how our creations perceive us, since they will be participating in our lives, communities and society at large. This quickly slides into philosophy, but I would say that we want empathy to be an internal organizing principle of any thinking system we design.

It’s impossible to have a conversation about AI without also discussing the role of the data underlying the intelligence. Why does a “deep learning” system need so much data? 

Deep learning systems run on data that has been collected, curated and engineered in a deliberate way to solve a specific problem. The large quantity of data feeds statistical learning that is very granular—down to the individual pixel for an image-recognition system, for instance. Just as a study involving statistics requires a large sample size to be accurate, “deep” neural nets require an extreme amount of training data in order to learn to decipher features with accuracy.

We hear a lot about the importance of training the data in an intelligent system, which sounds like a repetitive and somewhat tedious process. If you need a million pictures in order to learn what a cat looks like, doesn’t that make you a slow learner? 

Deep learning, inspired by the convolutional layers of the human neocortex, is just one aspect of cognition. People can learn new ideas so quickly because the human brain contains many other structures that contribute to intelligence, including “thinking neurons” believed to bind themselves deterministically to a single concept. These other structures enable what AI researchers call “transfer learning,” the ability to leverage existing knowledge to quickly learn new concepts. As AI architectures advance, they will acquire some of these features and gain the ability to learn with less data.

At the other extreme from deep learning is the “symbolist” approach to AI, which uses formal logic to create massive ontologies (e.g. OWL  and Cyc) and smart expert systems capable of human-like deduction. Though the symbolist approach is out of fashion and even labeled the “old” approach to AI by many, it reflects a distinct quality of human thought not captured by the generic convolutional neural net. The creation of systems able to learn from sparse data is likely to involve the merging of these two paradigms.

Tell us a bit about the “transparency challenge.” How important is transparency to AI development from a technology standpoint? 

The “transparency challenge” in AI is an especially difficult problem for mathematically trained systems like neural nets. What deep learning systems provide today are single-function black-box operations. For example, a system may be created to recognize a face from a photograph, or to translate a sentence. When they are integrated into a traditional software system, such as a strategy game, they take on a logical, discernable role, but their inner workings remain opaque. Looking ahead to neuromorphic computers, we can imagine architectures in which all of the steps in a system are learned, including the algorithms for executing these steps. The result could be a new class of spookily intelligent black boxes.

The key to making these systems “transparent” may lie in their working memory—a system analogous to the previously mentioned strategy-game AI. We will be engineering these systems at a high level, defining operations for storing patterns, focusing attention on patterns and so on, so we will have the hooks to trace the flow of execution. We will be able to isolate individual learned operations much as the human mind associates concepts with words. Sub-systems, like vision, might remain opaque, but at the pattern or concept level, we will learn to engineer transparency and even introspection.

Bots seem to be everywhere now, but how much do they really understand? Are they all they’re cracked up to be? 
Companies keep adding features to their bots, like they would to any app, but we seem to be a long way away from having a natural conversation with a computer. Though these systems make clever inferences using a variety of methods and offer a natural way to perform certain tasks, they aren’t really a part of our world. They don’t have mental models of what it’s like to live a human life. Some might say that this understanding, too, will drop out of big data analytics.

However, many leaders in the AI field believe that true artificial intelligence will need to be based on embodied intelligence. This is the school of thought that a thinking machine needs to be physically sensing and even navigating the world. Though senses may seem like overkill for bots that help us shop, they could provide a unifying “format” for learned concepts, as they do in the human mind.

What will be the next AI breakthrough?  

We are likely to see extended periods of “me-too” applications, while the latest systems learn all they can learn, followed by surprising leaps. Today’s deep learning is very good about “understanding” patterns in large datasets, given a narrow problem-area. New neuromorphic designs are on the horizon, however. Spearheaded by researchers at DeepMind, complete computers have been built entirely with neural nets, progressing in sophistication from the neural Turing machine to the differentiable neural computer. Through the inclusion of read-write memory, an attention controller and other features, the latter design in particular has demonstrated an ability to derive algorithms and apply them in new contexts. An age of true “intelligence engineering” may just be beginning.

What will it take to get to the point where AI is not just supporting the human experience, but actually advancing it? 

I wish we could lay out a concrete roadmap for AI like the one the nanotechnology community created for their field in 2007. The breadth of AI research, ranging from biology to mathematics to philosophy, would make this extremely difficult, but we can begin by designing our relationship to AI—that is, how we want AI to fit into our lives.

AI shouldn’t be magic. If we don’t know how a model works, we can’t be sure why it fails, and even when it succeeds we are still excluded. Intelligent products are ones we either like, dislike or distrust.

We know already that we prefer AI systems to reveal their mistakes and invite us to correct them. In other words, we want relationships from artificial intelligence, not products. Looking ahead, we can extend this basic etiquette into the principle of transparency, aiming at systems able to both learn and explain themselves simultaneously. Products reflect a designer’s empathy; future AI systems will practice empathy, moment to moment, if we design them to listen and communicate.


About Sheldon Pacotti: 
Sheldon Pacotti is Senior Solution Architect at frog in Austin. Having studied math and English at MIT and Harvard, Sheldon enjoys cross-disciplinary creative projects. He builds award-winning software, writes screenplays for video games, creates software architectures for businesses and writes about technology. @NewLifeIneract

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How to establish your brand on Pinterest (and make it popular)

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How to Get Popular on Pinterest.png

This is a guest post by Larry Alton

If you’re a ‘picture paints a thousand words’ kind of person, then Pinterest may be a great option for you.

Its tight-knit community and visual focus make it an ideal board for circulating and popularizing your ideas. And while it may not be as popular as Facebook and Instagram, it still has more than 150 million active users each month.

Pinterest also has a number of advantages:

  • All content is publicly visible.
  • Content can quickly ‘go viral with far less effort than other platforms.
  • It provides monetization options for businesses and stores (e.g. buyable pins).

People use Pinterest in many different ways. But let’s say you want to create a business or personal brand account on it to build an audience and ultimately drive more sales. What’s the best way to maximize its popularity?

Creating the Account

First, you’ll need to create your brand’s account.

  • Find a target audience. There are millions of users on Pinterest. So what makes you unique? Decide on your target audience, and how you’re going to appeal to that niche.
  • Optimize your profile. With your target audience in mind, optimize your profile so it will appeal to that audience. Include keywords they might search for, and describe your brand as accurately as possible.
  • Plan a content stream. You’ll need some ideas for an ongoing content stream. What types of images will you post? Will they be photos or digital designs? What’s your primary subject matter?
  • Integrate your account. Like any social media platform, you should integrate your Pinterest account with the other communication channels you have in place. For starters, you’ll want to announce your new Pinterest presence on your company’s blog and other social media platforms.

Popularizing Pins

Once you’ve created your account, you can focus to optimizing and popularizing your individual posts, known as “pins.”

Post regularly, but don’t spam

Your pins should be regular without being overwhelming. Pinterest users like a steady stream of new content, but not as much as high-volume platforms such as Twitter. Pinning once a day is fine.

But while you may only be pinning once a day, you should check in regularly and be ready to communicate with your followers. It may be worth investing in a mobile hotspot so you’re never disconnected from your audience.

Sephora has nearly half a million followers, and is a great example of how to time your pins. They’ve pinned close to 12,000 items, and yet they never flood or spam their users. They generally post post once or twice a day with things like “Today’s Obsession” and “Makeup of the Day”. They’ve clearly worked out a pinning schedule that’s perfect for their audience.

Create boards

Pinterest lets you store related pins in folders known as ‘boards’. You can create as many boards as you like, and give them whatever name best describe the pins inside them (“Recipes”, “Interior Design”, etc.) You can make a board public or secret, and even create boards containing both your own content and content shared from other sources.

Whole Foods is one brand that’s exceptionally effective at creating and managing boards. They have more than 60 independently developed boards, each showing off recipes and ingredients in specific categories such as “summer recipes” and “Paleo”.

Be original

Pay attention to what your top competitors are doing, and learn from their actions. But don’t just take inspiration from their most popular pins. Look at what they’re not doing as well. If you want your pins to get attention, they need to show people things they’ve never seen before.

Take Japanese brand UNIQLO’s campaign to dominate the infinite scroll. They created elongated vertical images and posted them in just the right way to present the illusion of animation when users scrolled past. It was never tried before, and immediately caught the attention of thousands of users.

Create tall, defined images

Most of Pinterest’s users are on mobile devices, so your best bet is to create high-definition, vertical images that mobile users can access easily. Tall pictures that fill up the entire space offered by the newsfeed are more likely to outcompete images in the same feed. The effect becomes even more powerful when the image is dominated by a single color.

Limit the text

Pinterest is a visual board, so keep any text on your pins to a minimum. Include a few words if you need to explain what the image is about or announce the date of an upcoming event, but otherwise keep the focus on the image.

Offer practical information or tips

Pins have space for text as well as images. This is a great way to give your audience practical information or tips. And because they’ll want to share that information with their friends and family members, they’ll be more inclined to share your pins.

The “Build It!” board set up by Lowe’s is a great example. Its thriving  is almost entirely outsourced, giving independent bloggers and DIYers the chance to contribute their own projects, complete with how-to guides, for all their shared followers.

Repin other high-quality pins

Generate more attention for your own brand by repinning high-quality or popular pins from other brands—preferably ones that would also appeal to your target audience. This will increase your visibility in other Pinterest feeds, and help you define what your brand is and what it stands for.

You can even create boards specifically for user submissions. Anthropologie has been extremely successful with a board called “Your Anthropologie Favorites”. Fans are encouraged to tag pins with #AnthroFave, resulting in even more visibility. And with 768,000 followers, it’s a brand you can learn a lot from.

Once you’ve created your account and have a stream of optimized Pinterest content flowing, it’s only a matter of time before you start attracting followers. And once you have your audience, you can nurture and tweak your strategy to grow your business – and your sales.

Larry Alton is an independent business consultant specializing in social media trends, business, and entrepreneurship. Follow him on Twitter and LinkedIn.

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A Desert Island Paradise … and a Great Podcasting Course

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A Desert Island Paradise ... and a Great Podcasting Course

On Monday, Stefanie Flaxman showed off an incredibly easy (no, really) way to boost the power of your content, make it more audience-friendly, and even enhance your SEO.

On Tuesday, things got a little silly when we asked our editorial team what their “desert island” copywriting technique would be. Come check them out — with examples ranging from the single word to a well-developed positioning statement.

And on Wednesday, Jon Nastor let us know that his popular Showrunner course for podcasters is open for new students — but only until next Wednesday, September 27. So if you’re interested, get all the details today.

Over on Copyblogger FM, I had a lively talk with freelance copywriter Chris Cooper (no relation to the well-known American actor) about how he made the shift to freelancing, what he thinks new freelancers need to know, and his favorite way to find new clients.

On The Digital Entrepreneur, Sean Jackson interviewed Kendall Guinn to learn the secrets to an engaging hyperlocal site — and the business models that can make this kind of site insanely profitable.

And on the Sites podcast, Jerod Morris addressed that enduring content question: How do you decide which content to charge money for and which to give away for free?

That’s the content for this week — have a fantastic weekend!

— Sonia Simone
Chief Content Officer, Rainmaker Digital

Catch up on this week’s content

by Stefanie Flaxman

by Sonia Simone

by Jon Nastor

by Jerod Morris & Jon Nastor

by Sean Jackson

by Sonia Simone

by Jerod Morris

by Kelton Reid

by Jerod Morris & Jon Nastor Source:

Nine Principles for Designing Great Data Products

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From data collection to analysis, to the usage and communication of results, using data to deliver quality customer experiences takes careful consideration. Here are nine principles for designing engaging data products that your users will love.

1. Collect Data Passively
Collecting user data should never interfere with the quality of the user’s experience. While privacy is certainly an important issue, consumers have growing expectations for how their shared data can be used in exchange for better experiences. This is especially true for millennials—80 percent say they have some or a lot of trust in the companies they do business with to keep their personal information secure. Smartphones present an enormous opportunity for passive data collection: accelerometer data, GPS data and app usage data can all be used to learn about your users and to provide better user experiences. Google Maps uses GPS data from its millions of users to provide the fastest routes and help users avoid traffic.

Passive data collection also unlocks hidden business value. For example, most companies collect clickstream data, but few go beyond the use of standard analytical solutions. At frog, we helped a Fortune 100 client assess one year’s worth of clickstream data from its web application to better understand their users’ behaviors. What we found were ways to redesign the interface to help more customers complete transactions digitally, rather than contact the call center. To do this, we generated User Interaction Flow Diagrams, which visualized the flow of users through the experience and allowed us to find pain points. Then, the data was used to generate user behavior models able to estimate the impact a UI change would have on the overall digital completion rate. The result is a better experience for customers—and call center cost savings of over $600k per year.

2. Don’t Exhaust the User
Until a user interacts with your product, no data exists and personalization is impossible. Active data collection techniques can help your product overcome the cold start problem, but they have to be a natural part of the experience. Successful data products get around this by giving users an easy and engaging onboarding experience capable of collecting the necessary data without being overly burdensome. Apple Music asks new users to Tell us what you’re into and presents a few bubbles containing genres to select. Stitch Fix guides users through a questionnaire that helps ensure their first Fix contains items they’ll love. Netflix asks new users to select three movies they like at sign up; they handle the rest over time.

Onboarding surveys aren’t the only solution. Frog worked with a Fortune 500 financial client to design a web application for helping customers find the perfect car. We built a sophisticated recommender system that utilized the attributes of every car on the market, as well as the preferences of users that interacted with the application. However, the application was unable to provide recommendations to new users. We could have designed an onboarding survey to get around this problem, but it would have been a tedious experience. Instead, we used existing survey data to assign attributes such as Sporty, Family Friendly, Luxury or Unique to each automobile. Then, we presented users with an interface that allowed them to toggle between these attributes and view recommended vehicles with a single click. The aspirational nature of the attributes kept users engaged—and we were easily able to collect large amounts of user data that could be fed into the recommender system.

3. Constantly Validate with Data
Launching your data product is only the beginning. Once users start to engage, it’s important that you are continually validating your data product by tracking important, quantifiable metrics. The world is always changing, and a model that works well today will not work well forever. Additionally, tracking important metrics gives you the ability to perform experiments, or A/B tests, that can help you improve the performance of your data product. Airbnb is constantly running hypothesis driven experiments by iterating on the user experience and product offerings. This includes anything from changes to the appearance of the website to optimizations for their smart pricing algorithm. By leveraging an internal tool used to perform A/B testing, Airbnb can measure the impact changes have on important metrics like click-through-rate or the number of bookings.

While collecting user feedback to improve the user experience is important, the best data products can instantly and automatically incorporate feedback in to the overall experience. For the vehicle recommender application, frog added a button to recommended vehicles that allowed users to place the vehicle in their Garage. This let users view all their favorite vehicles on a single page, but alsoprovided an excellent mechanism for us to collect feedback. This feedback was stored in our database, which we used to calculate the recommendations in real time. By storing user feedback in the same database that was used to make recommendations, the performance of our vehicle recommendation system improved as the number of users grew.

4. Give Users Control
An overly eager machine learning system that makes too many decisions, however accurate, will leave users feeling bewildered and frustrated. Yet, striking that perfect balance between anticipating needs and giving users the right amount of control can be challenging. Designers at Nest Labs learned this principle through experience: Making users fight against temperature schedules they did not select or want caused not only irritation and discomfort, but also thermostat usage that resulted in higher energy usage than before. By nature, people don’t like being told what to do. For the Nest Thermostat, letting users feel in control led to a better experience and to increased energy efficiency. Their initial Auto-Scheduler algorithm was optimized to reduce energy costs, but because they failed to take the end user experience in to account, this algorithm led to higher energy usage. The Nest designers listened to their users and updated the Auto-Schedule algorithm to ensure comfort and respect user inputs.

In a frogVentures collaboration with Heatworks to bring the MODEL 3 connected tankless water heater and app to market, one of the primary goals was energy conservation. Part of this conservation is achieved through increased heating efficiency. However, the majority comes through encouraging users to use less hot water. The straightforward solution to increased energy efficiency would be to put strict limits on the amount of hot water a household could use in a day, but that would lead to frustration and attrition. Instead, the data collected by the MODEL 3 provides the user with historical savings, goals and recommendations that encourages the user to take control of their own water conservation.

5. Meet Unexpressed Needs
Collecting user behavior data, passively or actively, is only part of creating great data products. It’s also crucial to understand how to use that data to anticipate the needs of users and respond accordingly. Tracking clickstream data, purchase data and any other user behavior data gives us the opportunity to create models of customer behavior that can be used to predict future behaviors. It also helps segment users into groups to develop personalized recommendations. Predictive texting on your iPhone, Netflix’s personalized recommendations or Mint’s budgeting advice all rely on massive amounts of user behavior data to provide you with timely information that meets your needs. Despite the wide variety of uses, these predictive applications all rely on the same approach: finding correlations in historical user data that can be used to predict unmet needs.

Connected vehicles represent a new opportunity to collect massive amounts of user behavior data that can be utilized to anticipate the wants and needs of the user. Working with a Fortune 500 insurance client, frog designed and built a mobile application that collected driving behavior data from GPS and accelerometer smartphone data, as well as an Automatic ODB port reader. The mobile app used this data to offer timely, location-relevant promotional deals and financial advice, as well as encourage safe driving behavior.

6. Invoke Discovery and Delight
Recommender systems are one of the most common types of data products. Providing your users with high quality recommendations keeps them engaged by providing personalized content and product recommendations. But what is a quality recommendation? Simply providing the most relevant recommendations can lead to obvious or boring results. To truly capture the attention of users, we need recommendations that invoke discovery and delight—serendipitous content users will enjoy but wouldn’t have thought of on their own.

Out-of-the-box recommender system solutions provide a passable experience, but it will take a truly bespoke solution to create a sense of discovery for your users. At frog, we developed a web application to help users find the perfect college. Behind the interface is a hybrid recommender system that combines content recommender (in this case, a list of schools) and collaborative recommender (schools similar to ones you like) systems. A hybrid approach offers relevant and serendipitous results, and leads to a richer customer experience.

7. Build Trust with Transparency
Even if your data product is working properly, users will be skeptical to engage with your product if they don’t have any understanding of how a decision was made. Providing transparency into the inner workings of your data product can help earn the trust of users. For example, Spotify will make recommendations with the tagline Because you listened to… By providing this information, users will have a better understanding of what to expect and can make a better-informed decision about what to listen to next. Many machine learning algorithms generate a probability or confidence score in addition to a prediction. Sharing these confidence scores with users can help them make informed decisions. This is commonly used in weather forecasting, where users are given a percent chance of rain instead of a binary result of rain or no-rain.

Transparency is especially important in areas such as healthcare and finance, where decisions can have major consequences. Working with one of the largest banks in Mexico, frog created a dashboard to be used by bank employees assisting customers. This bank served primarily low-income customers, who tended to complete transactions in person. With this insight, we created a dashboard for bank employees that displayed relevant customer information. It then presented a set of recommended actions and financially responsible guidance personalized to the customer that a bank employee could then offer. For each recommendation, the system displayed the rationale, citing relevant information such as recent life events or payment history. Including this extra layer of transparency allowed bank employees to have confidence in their recommendations.

8. Visualize the Complex
Making data easy to interpret is essential when designing great data products. Wayfinding apps help commuters easily avoid thick red lines of heavy traffic. Fitness tracker apps show simple charts and trend lines that can be understood at a glance. News sites like FiveThirtyEight use data visualization to help readers understand complex stories and concepts. Data visualization is everywhere, yet visualizing data without becoming overly complicated or busy is a difficult balance to strike. Making careful use of location, shape, color, size, weight, motion and other means of visually encoding information draws attention to important information.

Data visualization becomes increasingly challenging as the size and complexity of the data to be visualized grows. This challenge is especially common in IoT applications, where massive amounts of streaming data need to be visualized in real time. At frog, we worked with a leader in the oil and gas industry that was collecting sensor data from the instrumentation in their drilling equipment to assess the integrity of their wells. By employing a user-centered design approach, frog created a data visualization dashboard that allowed both technical and non-technical employees to make decisions using this complex data.

9. Blend In

Sci-fi movies portray a future where machine learning and AI exists among us as robots, intelligent chat bots and fully autonomous vehicles. While many articles about machine learning focus on these far-fetched realities, the truth is that machine learning is already in our lives today in much more subtle ways. Often, users are unaware of the sophisticated machine learning that powers their favorite products. They simply notice the improvements these algorithms inform. The lesson here is that the best data products will be ones that work with the way people live today by integrating with the products they already use.

At frog, user experience is always top of mind, and we perform extensive research to ensure the products we design fit in to the users’ lives. When designing data products, it’s tempting to create futuristic products that force users to change their behavior. While it’s important to push the boundaries, the best way to keep users engaged is to create intelligent, responsible data products that fit seamlessly in to customers’ lives.

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Success Has Nothing To Do With Your iPhone Model Number.

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Success Has Nothing To Do With Your iPhone Model Number.

Recently, another new iPhone model was announced. Working in tech, people asked me when I’d be buying it. I told them probably not anytime soon, if at all.

“A piece of metal is never going to define my level of success and it shouldn’t define your success either”

Before you buy anything, think about why you’re making the purchase. We often make dumb decisions about buying stuff because we don’t think it through properly.


It’s a piece of metal

Before you have a giant orgasm over the new iPhone, remember that it’s just a chunk of metal. You’ve been using a chunk of metal as a phone for over a decade now. It’s not going to get your rocks off any more than the last phone you bought.

The new iPhone is not going to make love to you although it might remember your name and say it in some sexy, fake voice, so you feel like it’s your friend.

The iPhone is not your friend; it’s your enemy. A chunk of metal doesn’t define your success.


It doesn’t make your life better

If this chunk of metal – called an iPhone – really made our lives better then why are we more depressed than ever? A new phone is going to make you happy for about 3.1 seconds and then like a goldfish, you’ll have forgotten how privileged you are even to own one, as well as afford one, shortly after that.

Only you can make your life better by making better decisions. Choosing not to let material possessions own your life and your time is one way to make your life better. Say no to a new chunk of metal because it’s not making your life better.


Has anything really changed?

Between each of the iPhone models, it’s basically the same phone. Each time they change the screen size to indulge our ADD (Attention Deficit Disorder) minds, but that’s about it. Think about it carefully.


That money, compounded, is more valuable

Read any of Warren Buffet’s or Tony Robbins books and you’ll see that the $1000 you shell out for a new iPhone is far better put towards investing. Invest in an index fund, invest in yourself, or use that $1000 to book a holiday so that you have something to look forward to and motivate you for the next six months.

The longer your money stays invested in one of the above, the more it compounds your results. Whether that is financially, personally or from a health point of you. Compounding wins every time.

“You don’t need a new chunk of metal; you need to invest instead”


Never follow the trends – create your own trend

Trends often fade away and a new iPhone is no different. Create your own trend. If everyone else is buying a new iPhone, then do the opposite. Don’t let marketers and technology companies tell you how to live your life. Live your life how you want to.


Are we more productive?

No freaking way. We’re more unproductive than ever and we consider way too many things because our ugly chunk of metal gives us unlimited opportunities to say yes to. Right now, your phone will allow you to book a tantric sex class that begins at 6 am somewhere near you if you really want.

You can literally learn anything at any time if you really want. My question to you is, does it really matter?

Even though you have unlimited options to be productive, you still procrastinate more than ever and so do I. We could be hyper-productive but we’re not and that’s okay. No chunk of metal is going to run your life for you and make you successful.


We don’t need even more distractions

My life already sucks because I get 101 notifications from WeChat, WhatsApp, Messenger and my three email addresses. It’s a full-time job managing all of this and I don’t buy into it. I don’t need to be always contactable – I need a life.

I’m not a robot and I’m not answerable to anyone. Think about this: Are you a free human soul or do you need to be told what to do by your phone?

I’m seeing more human disconnection than ever. At work, it’s easier to call people that are sitting next to me than it is to have a face-to-face conversation. Face-to-face conversations have become a battle between the other person looking down at their phone and occasionally glancing up to look you in the eye.

“All of us are sexier than an ugly piece of metal and we deserved to be looked at!”


Will I also be adding a new Apple Watch to my setup as well?

Not in a million years amigo. A watch is strapped to me and tells me everything via a tiny little screen. Can you imagine being in an intimate moment with your significant other and the watch is flashing and beeping at you? It’s enough to ruin anyone’s romance time.

The Apple Watch reminds me of a bracelet that future prisoners will wear to track their movements. I don’t intend on wearing an Apple Watch so I can be a prisoner in my own life. Life is hard enough already without having to be chained to technology.


Lastly, I’m enjoying aeroplane mode a lot these days

I could buy a new iPhone but I just love aeroplane mode way too much these days. Having the world of social media switched off and not being “ONLINE” all the time has given me space to think. In these brief moments of thinking I’ve been able to:

– Write inspiring blog posts that have gone viral
– Fall in love again
– Work on my health
– Read books and gain new skills
– Socialise with friends
– Mentor young entrepreneurs in a startup accelerator

Through these list of activities, I’ve been able to create more success than I could ever have imagined on my useless chunk of metal called an iPhone.

So honestly guys and girls, when people ask me if I’m buying a new iPhone, all I can say is “No I’m not buying a new iPhone because my life is more important. The human race and changing the world is more important.”

I need time to change the world and space to think; the new iPhone can’t do this for me and it never will.

No chunk of metal should ever define you and your success.

If you want to increase your productivity and learn some more valuable life hacks, then join my private mailing list on Source:

Conspirators in their own memory loss – findings from 53 patients with “psychogenic amnesia”

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27915675_5e0d733aae_b.jpgBy Christian Jarrett

A person diagnosed with psychogenic amnesia complains of serious memory problems, sometimes even forgetting who they are, without there being any apparent physical reason for their symptoms – in other words, their condition seems to be purely psychological.

It’s a fascinating, controversial diagnosis with roots dating back to Freud’s, Breuer’s and Charcot’s ideas about hysteria and how emotional problems sometimes manifest in dramatic physical ways. Today, some experts doubt that psychogenic amnesia is a real phenomenon, reasoning that there is either an undetected physical cause or the patient is fabricating their memory symptoms.

In a new paper in Brain, a team of British neuropsychologists have reported their findings from a study of 53 patients diagnosed with psychogenic amnesia – one of the largest ever studies of its kind. Michael Kopelman at Kings College, London, and his colleagues conclude that the prognosis (that is, the scope and speed of recovery) for psychogenic amnesia is better than previously realised and that there appear to be four main categories of the condition.

The patients with psychogenic amnesia were all patients at St Thomas’s Hospital in London between 1990 and 2008, and the researchers compared their memory functioning and clinical history with 21 patients with memory disorders with a known physical cause (such as early stage Alzheimer’s or hypoxia), and 14 healthy volunteers.

The patients with psychogenic amnesia fell into four distinct categories. There were those who were in a fugue state, who had been wandering lost for days with no recollection of who they are or their past life. “I had a breakdown,” said one patient. “My brain decided to close down. I felt as if placed into a grown-up body without knowing the history of the body.”

Upon neurological examination, the fugue patients appeared healthy, and their state usually returned to normal within four weeks, though usually sooner, and sometimes within hours. After recovery, most of their memories returned, except for a blank gap during the fugue state.

The second category was fugue-to-focal retrograde amnesia. These patients started out in a fugue state – lost and usually with no or little memory of their past and no sense of identity – then as the fugue state resolved, they were left with more persistent memory loss for large periods of their past lives. Their memory gaps seemed to take longer to recover than the fugue patients (sometimes never recovering), though with a relative sparing of more recent memories.

The third category was focal retrograde amnesia. These patients had a severe loss of memory for large periods of their lives, or their entire lives, sometimes a temporary loss of identity, but there was no fugue period involving wandering. The onset was often a mild neurological event (such as a minor stroke) or minor head injury, but one “insufficient to account for the severity of the retrograde memory loss”. Similar to the fugue-to-focal retrograde amnesia category, these patients’ memory loss was more prolonged than the fugue patients, but with a relative sparing of more recent memories.

And finally, some of the patients fell into a category the researchers called “gaps in memory” – they didn’t have a wandering period, loss of personal identity was also rare, and their one or more periods of memory loss were discrete, often tied to a specific traumatic experience (and often associated with PTSD).

At six months follow up, the fugue patients and to a lesser extent, the focal retrograde amnesia patients, showed good improvement. “In summary, the prognosis in psychogenic amnesia appears better than the previous literature suggests,” the researchers said.

Comparing the psychogenic patients with the neurological and healthy controls, the psychogenic group were more likely to have suffered a past head injury (though not of sufficient seriousness to explain their memory problems); they were more likely to have a diagnosis of depression; to have a history of family or relationship problems, or employment problems; problems in childhood; and/or a history of alcohol or drug problems.

The finding that the psychogenic patients were more likely to have a history of head injury than the neurological controls is particularly surprising. Kopelman’s team said “this may predispose some individuals to developing psychogenic amnesia at a later time of severe precipitating crisis.”

A debate about psychogenic amnesia that dates back to Freud is whether the process of memory loss is deliberate or subconscious. Kopelman and his team observed that their findings were more consistent with there being a conscious, deliberate element to the condition (as first proposed by Freud and Josef Breuer, though Freud later changed his position). For instance, some of the patients in the new study made comments like: “It’s like a box locked away, and I don’t really want to open it” and “I put things in boxes … I know the memories are there … but cannot get access to them.”

The current thinking of Kopelman and others is that the deliberate memory suppression of psychogenic amnesia is often brought about by stressful crises in life, and that the deliberate forgetting manifests in genuine neurological processes that really do interfere with memory and even personal identity. Kopelman and his team conclude by quoting the Cambridge University psychologists Michael Anderson and Simon Hanslmayr: “Control mechanisms mediated by the prefrontal cortex interrupt mnemonic function and impair memory … We are … conspirators in our own forgetting.”

Psychogenic amnesia: syndromes, outcome, and patterns of retrograde amnesia

Image: via César Astudillo/flickr

Christian Jarrett (@Psych_Writer) is Editor of BPS Research Digest Source:

FactCheck: No evidence for Jacob Rees-Mogg’s food bank claims

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The claim

“The Conservative government allowed Jobcentre Plus to tell people that food banks existed, and the former Labour government would not tell them. And that was a policy decision to stop people knowing that there was help available … The real reason for the rise in numbers [of food bank usage] is that people know that they’re there – and Labour deliberately wouldn’t tell them.” – Jacob Rees-Mogg, 14 September 2017

The analysis

The UK’s largest food bank network, the Trussell Trust, requires users to show up with a voucher from a recognised agency. These can include doctors, social workers, Citizens Advice or statutory organisations.

A voucher enables people to get a minimum of three days’ emergency food.

When the Conservatives came into office in 2010, they announced an official referral scheme with Jobcentre Plus, meaning staff could formally refer jobseekers to their local food bank.

However, by 2013, this had stopped as a national scheme. The Department for Work and Pensions (DWP) told FactCheck that formal referrals were replaced by simply “signposting” the local food bank.

In 2013, a minister confirmed that the DWP “is only a signposting body that does not get involved in any decision to award a food parcel. The act of signposting to any local organisation including food banks is not a formal referral or endorsement on the part of the DWP.”

It’s true that the national policy is still that Jobcentre Plus still makes people aware of food banks – and can refer them to a third party (such as Citizens Advice) – who can then issue a voucher. But it does not issue vouchers itself.

All this is at the discretion of district managers at the Jobcentre Plus, not the government.

According to the Trussell Trust, the reality on the ground is mixed. Some job centres don’t engage with their local food bank at all; some tell people to go to Citizens Advice or another third party who will then issue a voucher; while others apparently continue to issue direct referrals.

A Trussell Trust report in 2015 said that 60 per cent of its food banks reported having a good relationship with a local JobCentre Plus.

But it added: “In England, positive relationships between food banks and JCPs appears to have largely occurred due to actions of individual food banks or job centres, rather than as a result of ministerial efforts to ensure DWP agencies better address the needs of food bank clients.”

As for what happened before the Conservatives came into office in 2010, the DWP told FactCheck that it didn’t have any information about the policy of signposting under previous governments. The department said it was perfectly possible that, on a local level, individual job centres may have worked with food banks, but there is no national record of this.

Impact on food bank usage

Even if we forget about government policy, what impact does awareness raising by Jobcentre Plus have on food bank usage?

Despite pressure from campaigners, the government does not hold any information about the numbers of people who have been referred to food banks by Jobcentre Plus over the years.

However, the Trussell Trust commissioned an analysis of its data between 2014 and 2017, to see how many direct referrals came from Jobcentre Plus. The results suggests that these only account for a very small proportion of the three-day food supplies it hands out. In fact, the proportion has decreased.

In 2014-15, only 5.5 per cent were directly referred by Jobcentre Plus. This went down to 4.9 per cent the following year, and then to 5 per cent in 2016-17.

Of course, these figures need to be viewed very cautiously as they only account for direct referrals. It is possible that a significant number of people were referred via third parties, and who would not have otherwise used a food bank.

However, experts do not believe the role of Jobcentre Plus to be very significant in explaining the spike in food bank usage. Indeed, in 2014 a parliamentary committee looked at this exact question. Giving evidence, the director of Oxfam’s UK Poverty Programme, Chris Johnes, said this was “unlikely to be a major factor”.

Increasing number of food banks

Government welfare policy has been widely acknowledged to be a key factor in explaining why food banks are used so much.

The latest Trussell Trust statistics show that in more than a quarter of all cases, the primary reason people are referred to a food bank is that there has been a delay in their benefit payments. A further 17 per cent are referred because of changes to their benefits.

Indeed, the Trust says: “Our research shows that nationally, food banks in areas of full Universal Credit rollout to single people, couples and families, have seen a 16.85 per cent average increase in referrals for emergency food, more than double the national average of 6.64 per cent.”

But, of course, these referrals could not be made at all if food banks didn’t exist. So why are there so many? Is government policy to blame for this as well?

Clearly, a significant part of the reason must be demand – after all, there is no point setting up food banks in a places where everyone is rich and doesn’t need help providing basics for their families. So government policy is undoubtedly an underlying cause.

But the idea of building a large network of food banks across the country, to act as a welfare safety net, has been a longstanding aim of the Trussell Trust. The initiative was established back in 2004, when Labour were in government.

The Trussell Trust’s 2007 annual report states: “The aim is to socially franchise the food bank project to other community groups throughout UK … The trustees set a target of establishing fifty food banks by the end of 2010.”

This makes Jacob Rees-Mogg’s comparisons to food bank usage under the Labour government not very meaningful. After all, it could be argued that developing a national policy for a small network which only exists in a few locations may not make a lot of sense – but when that network expands nationwide, it does.

The verdict

We can’t find any evidence to support Jacob Rees-Mogg’s claim that the increase in food bank usage is due – in any significant way – to the government allowing Jobcentre Plus to make people aware of them.

  • The comparison to Labour’s policy is not useful, given that food banks were not as widespread. But even if we make this comparison, there is no evidence to suggest that, under Labour, some job centres did not develop relationships with food banks on a local level.
  • The change in policy that Rees-Mogg seems to refer to has been watered down since it was first introduced. It is not national policy for Jobcentres to directly refer people to food banks.
  • The referrals that do take place only account for a very small proportion of all referalls. And this proportion has fallen. Charities do not consider it a major factor.
  • Considerable evidence suggests that the government’s welfare policy is a key reason why many people need to use food banks. Indeed, research suggests that areas with full Universal Credit see far higher increases in food bank usage.

If Jacob Rees-Mogg offers any evidence to support his claim which we are not aware of, we will update this blog. Source: