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World Summit AI 2017 (11-12 Oct. in Amsterdam): The Takeaways

Summary by: Kristi Rohtsalu, 14 October 2017

Contents

Definitions and concepts .......................................................................................................................... 2

AI: Where are we right now? .................................................................................................................... 2

Future of AI ............................................................................................................................................... 3

Algorithmic equity ................................................................................................................................. 4

AI movie suggestions ............................................................................................................................ 4

Risks and challenges.................................................................................................................................. 4

Risks and what to do about those ......................................................................................................... 4

Issues to the industry ............................................................................................................................ 4

Social issues and questions ................................................................................................................... 5

Academic issues and challenges ........................................................................................................... 5

Role of humans in future .......................................................................................................................... 6

Applied AI .................................................................................................................................................. 6

Practical suggestions ............................................................................................................................. 6

AI powered ............................................................................................................................................ 7

Companies developing AI ...................................................................................................................... 7

Start-up & VC arena .................................................................................................................................. 9

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Definitions and concepts

• Artificial Intelligence (AI)

• Machine Learning (ML)

• Deep Learning

Artificial Intelligence is broader field, initiating intelligence. AI could be described as the attempt to make

machines think like humans. It’s an idea that’s more than 70 years old.

Machine Learning is subdiscipline of AI. If AI were a person then ML would be the brain.

Deep Learning, a system based on a deep neural network, is a subdiscipline of ML. This network is

modelled on the human brain and is ‘deep’ due to its numerous layers.

There are no ‘official’ definitions. It may not even be desirable to have final definitions at this early stage

of AI development.

AI is like an iceberg. Bottom up:

• “Dark data”, i.e. Data which needs to be understood before it becomes useful;

• “Light data”, i.e. Information;

• [Water line]

• Insight – generates downstream savings;

• Value – also generates downstream savings;

• Unexplored value – if extracted, will generate upstream savings.

AI: Where are we right now?

• AI is a great buzzword; it is overhyped; people have been overpromised. We are not really as clever

as people have made to think.

o Still, we may be there faster than many think; risks and downsides have to be considered

already now.

• Today’s AI is not really a general AI, but Machine Learning with lots of data. There is no real AI.

• Deep learning is good at certain aspects of perception, but not in others.

o E.g. in visual search: it is rather easy to find similar objects, but it is very difficult to find exactly

the same object.

• There is a huge gap in what vendors are promising and what they are actually able to deliver.

• AI is a massive commercial opportunity; that’s why so much intensity. (Nearly 2,500 visitors from 60

countries were there in the conference.) The base has been built; AI is reaching industrial scale right

now. AI is at the inflection point.

o “Two AI-s walk into a bar. The bar gets 10-billion-dollar valuation at an instant.”

• AI is moving to the edges (i.e. to the systems functioning in device. This is because of: privacy issues,

reliability concerns, legacy, and because device is effective level. We need AI systems that are able to

compute in the device. Devices should be able to:

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o Perceive,

o Reason (i.e. analyze the observations), and

o Act.

• Still missing in the development of general AI:

o Deep understanding of language;

o Integration of learning with knowledge;

o Cumulative discovery of concepts, theories, actions (algorithms always start learning from

zero; that’s why they are data hungry / need Big Data);

o A few other things (but not many).

o Adding missing parts requires conceptual breakthroughs.

• Reasons for optimism:

o Huge volume of data on human choices (documented since antiquity)

o Strong economic incentive to do things right

The five epochs of AI:

• Epoch #1: Knowledge (smart search)

• Epoch #2: Interactive (Learn from the past situations and propose plan for action.)

• Epoch #3: Co-active

• Epoch #4: Intent recognition / true AI

• Epoch #5: Autonomous AI = the next level in AI => Only at this stage we will have real issues.

Future of AI

• Development of AI is more evolutionary than it was with the internet.

• Cognitive systems; Co-evolution of humanity and AI

o Common sense is the ability to fill in the gaps.

• AI is training AI / Models are training themselves / Automation of Machine Learning

• Technological singularity. Prepare for a major economic disruption:

o Digital Darwinism

o Virtual Agent trends

o Proliferation of Hyper Reality

• The more we learn the more there is to do.

• Agile governance (governance ≠ government)

• The next AI revolution will NOT be supervised.

o Model training approaches: reinforcement learning, supervised learning, unsupervised

learning. We are talking about Learning Predictive Forward Models, Predictive Models in

Question-Answering System, Predictive Models Under Uncertainty etc.

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Algorithmic equity

• Countries are false constructs.

o Consider: Administrative map vs Ethnical map vs Community map.

o Yet countries are still needed for certain purposes (water supply, security, …), so we are

talking of Sovereign Plus.

• Lack of data = Asymmetry

• If you would be able to give your child just 1 GB of data (in whichever format, incl. pics and videos),

what would that 1 GB be? (A question concerning schools in rural areas.)

AI movie suggestions

• “Black Mirror”

• “Moon”, the robot in the movie

Risks and challenges

Risks and what to do about those

• The biggest threat are we ourselves / users of the technology:

o We don’t pay attention and regulation does not keep up => Solution: ‘Responsible AI’

(accountability, transparency)

o Intentional misuses of AI (e.g. autonomous weapons by those who want power)

• People / Us:

o Computationally limited

o Inconsistent preferences

o Internal conflict

o Nasty (malicious joy)

• Lack of diversity, data bias and algorithmic bias will lead to unforeseen and potentially dangerous

consequences. (“Growing role of AI in our lives is too important for leaving to men.”)

We had better be quite sure that the purpose put into the machine is the purpose which we really desire.

YOU CAN’T FETCH THE COFFEE IF YOU’RE DEAD.

Changing the way, we think about AI / making AI safer:

• Today: Intelligent machines that optimize a given objective

• Right: Provably beneficial machines

Issues to the industry

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• Majority of the Big Data projects fail: the projects do not deliver on time and/or on budget, or do not

deliver at all.

• Energy consumption: For the same tasks, AI spends 1,000x more energy than a human (2,000 kcal a

day for a human vs 2,000,000). AI is computationally very intense.

• Dialog systems / having dialogue with AI: currently poor.

• It’s still a ‘black box’ for the industry what Deep Learning can and cannot do.

• Taking AI actually to real world:

o AI is not reliable enough.

o Psychological barrier: Are we ready to give up control?

Social issues and questions

• Will AI make us smarter? Or: We will forget what we once could do by ourselves because now AI is

doing it?

• Job losses vs job creation; skills gap

• Access to data: democratization of the data remains a question mark.

• Ethical concerns:

o Fairness

o Transparency

o Privacy

• Legal issues

• Think of artificial sexuality / sex robots, for example:

Fair question to AI developers: “What if you succeed? What if you succeed…”

Solutions:

• Responsible AI => But: how to ensure that AI is developed in responsible manner?

• Agile governance => Unclear how it would work

Academic issues and challenges

• Engineering AI is hard:

o Difficult to debug

o Difficult to improve incrementally

o Difficult to verify => How do we validate AI? What are the performance criteria?

• Statistics ≠ Knowledge

• The standard bias is the assumption that everything is learned. We do not know how people are

learning. What is the minimal set of qualities that evolution has given us? Currently, AI is being

developed on too narrow basis, assuming that math and programming are enough.

• The system must know what it does not know.

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• Corporate AI has short-term purposes, a la ‘How can we sell our ads better?’ Academic labs are

understaffed; four people in a lab cannot undertake a bigger problem.

• Deep learning challenges:

o Multimodal learning

o Reasoning, attention and memory

o Natural language understanding

o Deep reinforcement learning with memory

o Building Intelligent Agents

“The trick, William Potter, is not MINDING that it hurts.”

Optimism is a key.

Role of humans in future

Our main job will be asking good questions; AI will provide answers.

Invest into your right brain: imagination, emotions etc. Asking good questions requires creativity.

Applied AI

• AI needs an ecosystem: academia, business and people working together, e.g.:

o It’s two-way street with the academia; theory and practice have to complement each other.

Practical suggestions

• Disrupt or be disrupted!

• Implement technologies early on in order to collect data & build data asset.

• AI is a team sport, on every level of the organization.

• (If no C-level support) Start small with a proof of concept.

• Automation of the manual processes is typically the ‘lowest hanging fruit’. But watch for building a

faster horse instead of a car!

• Have a purpose / a business goal. Focus on business problem. AI may be the solution but does not

necessarily have to be. Do not do AI for the sake of AI.

o Presently, AI is widely used in advertising technologies and in product recommendations.

o Evolution of marketing: Product marketing -> Market segmentation -> Consumer

personalization -> Individual engagement. AI enables customer engagement.

• It’s time to embrace the vision: AI gives us the possibility to ask questions that have never been asked

before. The power of AI is to expand the limits. Reimagine business processes; become intelligent

businesses.

o Really ordinary things, not specific to AI: plan for success, know your customers, value

proposition etc. Vision -> Execution plan -> Only then climbing starts.

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o AI should be used in the areas where humans are poor (e.g.: digging through a big pile of data

and documents to find something in particular).

o Think how to assess AI performance.

• Don’t treat your data as a class of water, carelessly!

• Before you can do AI inhouse, you need to master Big Data.

• Human interface of the AI is the key. AI is the new UX. (Artificial Intelligence is the new User

Experience.)

• When talking with the vendors, avoid sales guys (sales guys know nothing) and talk with the engineers;

take your own engineers with you. Vendor selection is a challenge on its own. Don’t hesitate to say

‘Goodbye’ to a vendor, if the vendor does not deliver according to the expectations; avoid lock-up

provisions in the vendor contracts.

• Build your own technical teams.

• ‘Secret sauce’ for the retail companies, but not only retail: HI + AI + ML = Human Intelligence + Artificial

Intelligence + Machine Learning

AI powered

• Accelerating technologies

• Convergence of all fields

• Google & NASA developing quantum computing

• Zero employee robot factories

• Quantum teleportation of consciousness – it may be possible (?)

• We are moving:

o from simple, linear systems to complex adaptive systems

o from control to emerging and self-organizing teams

• Exponential organizations (ExO-s)

• Algorithmic organizations

• Quantum AI

Companies developing AI

Large B2C (Business to Consumer) companies are the first ones who are eager to apply Natural Language

Processing technologies.

Alibaba is developing Hangzhou City Brain.

Data -> Cognition (data understanding) -> Decisions & Optimization -> Search & Mining -> Prediction ->

Prevent accidents from happening.

Cameras are recognizing what is happening. Realtime decisions. Accidents are reported automatically.

Google Cloud is democratizing AI via APIs. E.g.:

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• https://cloud.google.com/products/machine-learning/

• TensorFlow (open source)

ING looks at the network of companies instead of solely focusing on individual companies. The problem

that they are trying to solve is the ‘ripple effect’: e.g. if a big supplier fails, several related companies may

fail too; this you cannot see when looking only at the individual companies.

From node view to network view. This is achieved by looking financial transactions.

With the knowledge of potential network effects, ING can help its customers to stay one step ahead which

in turn improves customer relationship.

Issues: bank secrecy and data protection. E.g.: data protection laws do not allow exploring transactions

between private individuals.

Intel: Advancing decision systems with complementary learning.

Train -> Test -> Deploy -> Update

Hierarchical learning / Global stable pattern + Cognitive approach / Dynamic information

Decision making is not linear; it’s a loop.

Components of complementary learning:

• Information extraction (ML and Deep Learning)

• Knowledge representation (autonomous instant learning)

Cognitive part: correlations between people, places, things, locations etc.

Areas of application:

• Supply chain intelligence

• Fraud detection (auto claims, credit card fraud)

• Intel Saffron AML Advisor => New trending product

• Intel Saffron FSI Early Adopter Program

Airbus Helicopters has been using an artificial neural network since 2005 to adjust its rotor blades.

Modelling challenge: How do you model, given zero tolerance towards ‘bad’ observations?

Chatbot Rose: You can meet Rose yourself at bit.ly/bot_rose

Airbus Smarter Fleet™ is cloud-based service platform developed by Airbus with the help of IBM. Smarter

Fleet provides Airbus customers with various solutions, including intelligent maintenance and engineering

tools, increased flight efficiency and optimised fuel consumption.

Swedbank’s Chatbot Nina answers 85% of questions correctly. In customer support, 3 people are now

able to do the job of 35 people. Nina is:

• Answering simple questions,

• Acting as proactive assistant,

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The next step is third party integration. E.g: You are paying the electricity bill. Nina asks: “Are you

interested in cheaper options?” You say: “Yes.” Nina searches for cheaper options and comes back with

the possible alternatives.

Phrasee is AI that writes better than humans. It can be used for writing marketing messages.

Everybody in marketing thinks that he/she is an expert in writing marketing messages – in reality nobody

is.

A little experiment: https://goo.gl/5s8SCE Note the disagreement in answers.

Booking.com took Google’s tag line “AI first company” and reworded it: “Customers first AI”.

It takes (much) more skills than just Machine Learning to have an impact.

The first hypothesis is rarely correct.

ebay.com: Deep science in everyday commerce

Chat with Shopbot like you would with a friend. Conversational commerce. Shopbot must learn

conversation; it must know about every product that is on sale.

“If all the AI capacity does not save time and/or money for our customers, we do not need it.”

Multi-model inputs: Allow users to interact seamlessly across different modalities and switch within the

same context: touch, voice, text, image, emojis.

People want to talk in emojis. They want to talk with an AI like with a human (the human face of AI).

Practical issues with recommendation systems: What constitutes a ‘great deal´? It’s more than price; it

includes size, brand etc.

AI helps to describe the item that the customer is looking for: intent, query understanding, graph, intent

change…

JP Morgan and Goldman Sachs are investing into the start-ups the services of which they are interested

in. For a day or two per week, they are inviting engineers of the start-ups to their own offices so that the

engineers can better understand the context.

Deutsche Telekom uses many AI vendors. Their experience:

• It’s easier to convince business people (cost saving – great!) than IT people (If something goes broke

with the AI, then they don’t know how to fix it.).

• Different vendors are good at different things; one vendor is not enough.

Start-up & VC arena

• A couple of years ago (in 2014 and 2015) 3-4 out of the 10 start-up companies said that they were

doing Big Data. Actually, the vast majority of them was only PLANNING to do Big Data and not doing

it. Now it’s the same with the AI.

o “AI is like teenagers and sex. Everybody is talking about it, nobody is really doing it.”

• Natural language processing is currently the hottest topic.

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• Forecast: A lot of VC money is going to AI start-ups. There will be lots of failures and consolidation. At

the end, there will be very few true AI only start-ups.

• VC questions:

o Where exactly the value is being created?

o From where do you get the data? What’s your approach to the data? (VC-s are looking for the

creative ways of collecting the data.)

• More and more applied AI is in narrow niches / in verticals.

• More entrepreneurs are coming from exotic countries (Vietnam etc.).

• An AI start-up typically needs to co-operate with someone that has the data asset, such as a ‘legacy’

corporation.

• In case of enterprise solutions, an AI start-up has to deal with the legacy systems that can be simplified

with the help of AI.

• It’s always team over the idea.

An AI company that is looking for the customers / partner companies, is looking for the following factors:

• The customer has data, and

• There is C level support (CEO, CTO), and

• There is a problem to solve.