Show and Tell AI Style
  • Screen Shot 2017-07-02 at 11.57.50 am

AI helps humans push our boundaries and augments our cognitive abilities…and why Harley-Davidson image?  More later.

 

Show and tell used to be my favorite time in primary school. It’s the one time a geek really gets to show his fellow students his awesomeness. So, I’ve decided to take the show and tell route to bring out the power of AI and how it is changing the world.

 

The ‘show’ is the image that headlines this blog which I created it in less than 10 minutes, using an AI powered tool called Autodraw by Google. Normally the drawing would not be much of a challenge to someone even slightly artistically inclined. But I confess I am not that.  It’s kinda strange, because my brother is the opposite, he is an architect and an artist.  Anyway, I was only able to do it because the tool read my rudimentary doodles and gave suggestions of what it thought I drew. To understand the power of AI, try your hand at creating a drawing, say a birthday cake, on MS Paint (I use a Mac, so I often use Pixelmator and the standard icons) and then do the same on Autodraw.

 

Artificial intelligence is fundamentally altering the very fabric of our existence. There is not a single aspect of our lives whether personal or professional that AI cannot significantly alter  or “augment” for the better. Take for example AI’s predictive analytics. The CIA is using an advanced version, to predict social unrest, extremist activities and threats to national security a week before they happen. If that reminds you of the 2002 Tom Cruise movie Minority Report, it is exactly that…except the prediction comes from an AI engine, not psychics.

 

Businesses are largely embracing the benefits of AI. Take the financial sector for instance, where AI-powered hedge funds have been in existence a while. Research shows these funds have begun to outperform their human counterparts year after year. Your savings are likely to earn more with an algorithm than a trained market analyst.

 

Another example I like to use is of Watson Ads and Campbell Soup on weather.com. With Watson ads, consumers could interact with the brand to get personalized responses. A typical question could be “what can I make for dinner tonight.” Rather than serve up pre-canned responses that you can get on any cookery site Watson’s responses would consider weather, time of day, even ingredients on hand to serve up a unique personalized recipe.

 

Finally, I come to how AI is making an impact on retail business. A Harley-Davidson dealer in New York found his sales leads increased by a whopping 2,930% when he used an AI-driven marketing tool. The increase came about as a result of an AI-driven marketing platform that works across digital channels like Facebook and Google. It measures and autonomously optimizes the outcome of marketing campaigns.   In the HBR story, an example was given that was cool, AI suggested changing very specific word elements in Ads that made a huge difference. They changed the word from “Buy” to “Call” which resulted in a 447% increase in response.  This means that AI is allocating resources to what is PROVEN and validated to work (through simulation and testing), thereby increasing digital marketing ROI.

 

At HIVERY too, we have used the power of AI to create unrealized profit in the retail business. Take for example our Vending Analytics tool. Created with the aim of optimizing vending machine profits, we seen an average increase of 15 percent in sales revenue and an average 18 percent reduction in restock costs for vending machines that use the system. We do this by analyzing data for each individual machine and optimizing the space, product mix and pricing formula per machine. That level of personalization is impossible to achieve without AI.

 

The way I see it, the goal of AI is to enable us to push the boundaries of what is possible by augmenting our cognitive thinking. The drawing would not be possible for me, just as a personalized recipe is out of the reach of most home chefs. So also the profits that we see accrue to our clients. That profit just did not exist before AI!

 

If you want to learn more about our solution then check out my Slideshare on HIVERY’s secret ingredients

 

 

  • Screen Shot 2017-06-24 at 9.31.19 am

What is Lean Design Approach and Handcrafting?

If you want your company to truly scale, you first have to do things that don’t scale. Handcraft the core experience to shit! This means you need to get your hands dirty and serve your customers one-by-one. In this podcast, Reid Hoffman talks to Brian Chesky, CEO of Airbnb, and goes through his early work on handcrafting solution. We called it an MVP or applying a “Lead Design Approach“. A way to discover, learn and validate ideas and design a possible product roadmap. It’s actually what we do at HIVERY. But more on HIVERY later.

 

In the podcast, Brian shares the imaginative route to crafting what he calls an “11-star experience.” I often call this “Thought Experiments” and it is an important design concept in applying Design Thinking/Human Centred Design (HCD) thinking to your product/service innovation.

 

Here are my key points of the podcast is this:

 

  •  Find passionate users – they can map your product roadmap. I often call them your “Extreme users.” These are those who are passionate enough to give you honest straight up feedback. The idea is to turn these feedback facts into real insights, these, in turn, are your Design Principles for your future product. It is the minimum you need to design into your solution to address their core pain points.

 

Remember I said “design principles” you are not implementing all their feedback as is. You should not see them as a list of features.

 

  • These are the users who feel the pain the most. These are the users who your product really matters so use to them co-design what is desirable, feasible and viable. The aim is to build your product roadmap for the future but with a focus on what you need to do/build today.

 

Powerful design question to use: “What could we do to surprise you? What can we do, not to make this better, but to make you tell everyone about it?”

 

  • By getting your hands dirty and “handcrafting” your MVP you gain insight never possible. By handcrafting, I mean you are basically going to “concierge-ing” the shit out of your MVP and later on automate (ie add technology, processes etc) what is important and what you need to do to scale and drive efficiencies.

 

  • This means you need to serve your initial users/customers one by one to gain this insight. Sit with them side by side, shadow them, observe them and build empathy and understanding. How else can you gain this insight to the problem?

 

  • With Airbnb, their first 10 customers were based all in NYC, yet they had their office in San Francisco, so they moved and visited them. Not to feel too creepy about entering their customer home, they say they are wanting to learn more about their users and in exchange, they provided them free professional photos to gain access to their users and insight to their service. They go a lot of feedback and ideas.

 

In fact, one of the extreme passionate customers had a booklet of notes for the Airbnb guys. This is what I call serious co-designing with your customers. 

 

  • Use “Thought Experiment” ( Thought Experiment is a process of the imagination used to investigate what may happen). Einstein is most famous for using this mental technique and help them came up with this his theory of Relativity.

 

  • The Airbnb guys used the concept of “11-star experience“. The “Nirvana product/service“.

 

  • The Airbnb guys went door-to-door, meeting Airbnb hosts in person – and shares the imaginative route to crafting what he calls a “1-star experience” to “11-star experience“. Need to think “extreme product/service” as your Nirvana, but build for today and think to scale up in the future. Example: Elon Musk wants to go to Mars (11-star product). How I get there? Need spacecraft, how do I fund spacecraft? Make it and funded by launching satellites for telco in the short term and build components for Mars.

 

  • In the podcast, they (Airbnb) talk about what “a 7-star experience” Through Experiment looks like…You knock on the door. Reid Hoffman opens. Get in. “Welcome. Here’s my full kitchen. I know you like surfing. There’s a surfboard waiting for you. I’ve booked lessons for you. It’s going to be an amazing experience. By the way here’s my car. You can use my car. And I also want to surprise you. There’s this best restaurant in the city of San Francisco. I got you a table there.”

 

As mentioned about, this is the framework is actually used at HIVERY. When we start any Artificial Intelligence (AI) initiative with our customers, we go through a Discovery-Experiment-Deployment approach. We essentially, we combine Science with Design and Design with Science.

 

DISCOVERY

Unpack business needs, data availability and success metrics conducted Thought Experiments a build data and user empathy.

EXPERIMENT

Co-design small experiments to validate system value

DEPLOYMENT

Map out the operational plan for full deployment and support

 

It’s in HIVERY’s DNA.

Enjoy the audio

 

Below is a summary of insights from the story published in New Scientist entitled The road to artificial intelligence: A case of data over theory

Dartmouth College in Hanover, New Hampshire

  • Team from Dartmouth gathered together to create a new field called AI in 1956 Create fields in: “machine translation, computer vision, text understanding, speech recognition, control of robots and machine learning
  • They took at top-down approach – reason logical approach where you first creating a “mathematical model” of how we might process speech, text or images, and then by implementing that model in the form of a computer program AND that their work further understanding about our own human intelligence.
  • The Dartmouth had two assumptions of AI
    • Mathematical model theories to stimulate human intelligence
      AND
    • Help us understand our own intelligence
  • Both assumptions were WRONG.

Data beats theory!

  • By mid-200s, success came in the form small set of statistical learning algorithms and large amounts of data and that the intelligence is more in the data than in the algorithm – and ditched the assumption that AI would help us understand our own intelligence
  • A machine learns when it changes its behavior based on experience using data which is contrary to the assumptions of 60 years ago, we don’t need to precisely describe a feature of intelligence for a machine to simulate it.
  • For example email spam, every time you drag it into “spam” folder in your Gmail account for example, you are teaching the machine to “classify” spam or everytime you teach for a bunny rabbit and go to images click “bunny rabbit” you are teaching the machine what a bunny rabbit looks like. Data beats theory!
  • For the field of AI, it has been a humbling and important lesson, that simple statistical tricks, combined with vast amounts of data, have delivered the kind of behaviour that had eluded its best theoreticians for decades.
  • Thanks to machine learning and the availability of vast data sets, AI has finally been able to produce usable vision, speech, translation and question-answering systems. Integrated into larger systems, those can power products and services ranging from Siri and Amazon to the Google car.
  • A key thing about data is that its found “in the wild” – generated as a byproduct of various activities – some as mundane as sharing a tweet or adding a smiley under a blog post.
  • Humans (Engineers and entrepreneurs) have also invented a variety of ways to elicit and collect additional data, such as asking users to accept a cookie, tag friends in images or rate a product. Data became “the new oil”.
  • Every time you access the internet to read the news, do a search, buy something, play a game, or check your email, bank balance or social media feed, you interact with this infrastructure.
  • It creates a “Data-driven” network effort a data-driven AI both feeds on this infrastructure and powers it.
  • Risk: Contrary to popular belief these are not existential risks to our species, but rather a possible erosion of our privacy and autonomy as data (public and private) is being leveraged.
  • Winters of AI discontent – the two major winters occurred in the early 1970s and late 1980s
  • AI today has a strong – and increasing diversified – commercial revenue stream

 

At HIVERY, we combine Design Thinking, Learn Start-Up thinking with Machine Learning techniques to develop and release “new to the world” solutions that are intuitive yet power by applying deep science to help solve complex business problems.

 

HIVERY applies artificial intelligence to complex business problems. We do this through our methodology of DISCOVERY, EXPERIMENT and DEPLOYMENT.

 

06]

 

  • Artificial intelligence (AI) includes:
    1. Natural language processing,
    2. Image recognition and classification
    3. Machine learning  (ML) –  so it’s a subset of AI and Deep Learning (artificial neural network –  more below) is a subset of ML
  • In 1950 Alan Turing published a groundbreaking paper called “Computing Machinery and Intelligence”.  Turning poses the question of whether machines can think?
  • He proposed the famous Turing test, which says, essentially, that a computer can be said to be intelligent if a human judge can’t tell whether he is interacting with a human or a machine.
  • Artificial intelligence was coined in 1956 by John McCarthy, who organized an academic conference at Dartmouth dedicated to the topic to explore aspect of learning “cognitive thinking” or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
  • The phrase “machine learning” also dates back to the middle of the last century.  In 1959, Arthur Samuel (one of the attendees of Dartmouth conference) defined machine learning as “the ability to learn without being explicitly programmed.”
  • Samuel to create a computer checkers application that was one of the first programs that could learn from its own mistakes and improve its performance over time.
  • Like AI research, machine learning fell out of vogue for a long time, but it became popular again when the concept of data mining began to take off around the 1990s.
  • Data mining uses algorithms to look for patterns in a given set of information.
  • Machine learning went one step further  – it changes its program’s behavior based on what it learns
  • Years go by with “AI Winters” due to lack of big data sets and computing power
  • Until to IBM’s Watson AI winning the game show Jeopardy and Google’s AI beating human champions at the game of Go have returned artificial intelligence to the forefront of public consciousness
  • Now Machine Learning is used for predicting and classification:
    • Natural language processing – IBM Watson is a technology platform that uses natural language processing and machine learning to reveal insights from large amounts of unstructured data.
    • Image recognition – i.e. people face Facebook with Deep face – https://research.facebook.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/
    • Recommender system –  Amazon highlights products you might want to purchase and when Netflix suggests movies you might want to watch and Facebook with newsfeeds.  HIVERY also uses Recommender system to help our customers with having the right products in the right distribution channels at the right time and place.
    • Predictive analytics –  HIVERY around in fraud detection, price strategy and new product distribution placement strategy
  • Deep learning  – often called artificial neural network or neural net is a system that has been designed to process information in ways that are similar to the ways biological brains work.
  • Deep learning uses a certain set of machine learning algorithms that run in multiple layers. It is made possible, in part, by systems that use GPUs to process a whole lot of data at once.

Source: http://www.datamation.com/data-center/artificial-intelligence-vs.-machine-learning-whats-the-difference.html