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The Future of Ai & Guiding Questions

At Flamingo Ai, we’ve worked hard to define the right

questions to place at the core of the Ai technology roadmap. And we’d like to share them with you.

The guiding questions

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For us, Ai is a synonym for machine learning – the practice of automation by teaching machines rather than directly programming them. In looking to the future of Ai, we believe that the tech alone is not enough. To be successful, we must learn to ask the right questions

“The scientist is not a person who gives the right answers, he is one who asks the right questions.” Claude Levi-Strauss

“Judge a man by his questions rather than his answers.” Voltaire

1 — Why is Ai important Now?

We live in a time where we can implement Ai – and we should. Recent advances in statistical processing have given us the capability to teach machines. Specifically, neural net processing and the rise of Google (the first algorithm company) have validated decades of research into machine learning.

Large companies and governments now understand the need to invest heavily and leverage the exponential impact that Ai technology can bring. The alternative is being swept away by more technologically savvy competitors. This is why Ai is important now

“Ai is one of the most important things humanity is working

on. It is more profound than…electricity or fire”

Sundar Pichai, CEO at Google

We also need machine learning more than ever; the amount of data generated online everyday has exploded – there is now roughly 20,000,000,000,000 gigabytes generated every year. That’s the equivalent of 60,000,000,000,000,000 novels and it’s too much information for people to read.

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* Source: www.eetimes.com

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We’ll find deeper insights from the data we’ve accumulated, and Ai will augment and replace the more cumbersome corporate decision making in place today `

Just as electricity revolutionized the modern world. Just as computers

revolutionized automation and process…Ai will revolutionize

decision making.

2 — How do we plan for Ai and get an edge?

To do this, we look to the next 3 years and make well placed strategic moves. In 3 years’ time, most companies will be incorporating machine learning into their corporate strategies. This is not a debated forecast amongst global experts. Smaller, nimbler “data science” teams will take corporate data repositories and operationalize them - unlocking the riches hidden in data warehouses.

3 — What will winning look like? And failure?

Winning comes from building smaller brains that can deliver proven results and then aggregating those brains into a bigger engine. Deploying big engines straight up will likely fail and of course they will - It’s too early in the evolution of this tech for them to succeed. Small, highly motivated teams with deep expertise will win. These teams will focus on deploying smaller brains for narrow, specific use case solutions using Ai technology. These are the teams that the Googles

Failure on the other hand will come from naive implementations of Ai with unrealistic expectations. We are already beginning to see the effects of wild promises with big and expensive implementations only to see underwhelming results. This is the failure of general Ai implementations

Paying a handful of developers with no relevant experience to build a ‘chat bot’ or a ‘machine learning platform’ is a formula

for failure.

and Facebooks spend millions of dollars acquiring and integrating. These are the quant teams that hedge funds and startup unicorns are built on. Some technology companies such as Flamingo Ai have these teams in house, and are looking for opportunities to marry their state-of-the-art technology to corporate partners and large scale customer data. This unlocks long-standing investments into data and gives clients access to technology previously reserved for Google or Facebook.

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In the future, most decision making will be made by machines. Gartner predicts 20% of all online content will be generated by machines by 2020; 30% of customer interactions will be handled by a machine by 2022; and 40% of financial services jobs will be automated by 2026. The world will have too much information and move too quickly for humans to bypass using the power of machine learning.

“Technology, through automation and artificial

intelligence, is definitely one of the most disruptive sources.”

Alain Dehaze, CEO at Adecco

In this new algorithm-based and software-first world, things will move faster than governments and legislation can keep up with. It’s vital we find a way for our businesses to fit and important to proactively lean into the technological advancements whilst injecting humanity into the process. That humanity comes from remembering our businesses exist to create value for customers and supporting our teams to do that as well as possible.

By integrating Ai technology expertise directly with smaller business units (unlike IT which emphasizes large projects using large infrastructure), these deployments can be highly focused, adding commercial value to the individual unit by drilling down to the human interactions they impact. This smaller footprint is more agile, specific to a business unit’s needs, and yet still feeds data back to meta-brains where organization-wide Ai data is processed and delivered in the form of insight

4 — How do we keep humanity present and prepare for the change?

Unsupervised learning presents a machine with data and asks the algorithm to tell us what is important. It works best with both small amounts of data and vast amounts of poorly understood data. Flamingo Ai’s approach is an unsupervised method, and what

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Much of the previous developments in machine learning have been around supervised methods

that are black boxes - they perform on well-understood data sets, but no-one really

understands why they give the answers they do.

we call white-box. Not only does the machine automatically organize our data, but we have complete access (with visualizations) to the insight it provides and it tells us why the machine thinks and behaves the way it does

“The thing that’s going to make artificial intelligence so powerful is its ability to learn, and the way Ai

learns is to look at human culture.”

Dan Brown, Author

Not all Ai is equal. There is a difference. Some machines are trained and others engage unsupervised learning to discover knowledge for themselves. Training machine learning algorithms (supervised learning) requires lots of very well

understood data, so the machine will have the right behavior afterwards. It also takes a reactionary team consistently re-training the machine as things change. This data not only has to capture the correct behavior of the machine, but also all the exceptional cases. Think of it as rules-based.

5 — What is the most common misconception to overcome?

Partnership over product

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An Ai future is an inflection point for executive decision-making. Company representatives will no longer exercise their judgement in isolated incidents with limited information. Rather, Ai engines will render recommendations based on historical and in our case, real-time data. The results will be astounding.

What’s more, mass scale human interactions can be automated or at

the least assisted using Ai to drive stronger results, better efficiencies

and greater quality control.

Every business is unique. Our belief at Flamingo Ai is that you don’t need just an Ai product or platform – you need a partner. A partner provides expertise and software ready to solve your specific business problems and values collaboration over plug-and-play.

The future driven by Ai is not yet written. Together and through partnership, we have the unprecedented opportunity to create vast good for people, whether they be customers, team members or communities

Your Conversational Ai PartnerNew York Sydney

flamingo.ai

hello@flamingo.io