#Enterprise #AI insights from the AI Europe (@AI_Europe) event in London
Enterprise AI insights from the AI Europe event in London
I attended AI Europe last week. It was a 2 day event dedicated to AI only – and it was well worth attending! In this post, I capture my key insights from this event. I have an interest in Enterprise AI due to my course Implementing Enterprise AI.
Over the two days, there were some key general themes in the event
AI is the new industrial revolution
Augmented intelligence is the near term future instead of Artificial intelligence
Superhuman capabilities (from machines) in niche application areas
Can humans handle exponential capabilities? Intellectually humans not equipped to deal with exponentials
Below are more specific insights including the speakers’ session names
Uber: Why Deep Learning?
For AI capabilities, we need
Ability to query inference across knowledge bases
Ability to understand Context through industry semantics
In the talk (Opening Speech Bringing machine learning to every corner of your business DR. Luming Wang, UBER” ) had a slide called Why Deep Learning which gave a concise need for Deep Learning
Why Deep Learning?
Better results than traditional ML or human in
Image recognition/classification/object detection/OCR
Super long sequence pattern recognition
Require few feature engineering
More data = better results
More data + deeper network = even better results
Easy to get reasonably good results
Uber: What are the challenges of Deep learning
What are the key challenges for Deep Learning? Another Uber slide
There are many frameworks and each has its pros and cons (Caffe, Tensorflow, CNTK, MXNet, Theano….)
If you get lucky, you’ll be very happy but otherwise, you may get frustrated.
Hard to understand
Hard to debug/fix/tune
Few theory guidance – super high dimensionality, vast amount of parameters
Very long training time – often in days or even weeks
Often require customized hardware for prediction
UBS: Potential applications of AI in financial services
Financial services will be the key drivers for AI. Annika Schroder(UBS AG) had an excellent slide on use cases clustered by financial services activities (Banking: why UBS is interested in AI and other fintech innovations ANNIKA SCHRÖDER, UBS AG)
Customer Service and Engagement: Conversational Interfaces, Virtual Advisors, Customer Service, Marketing, Smart Spending, Passion Fields, Client Segmentation, Sales
Investment and Trading: Investment Strategies, Investment Sentiment Analysis, Investment Reporting, Quantitative Trading, Investment Research, Investment Risk Management, Knowledge Platform, A.I. Trading
(Cyber) Risk and Security: Cyber Incident Investigation, Intrusion Prevention or Detection, Payment Fraud Detection, Authentication, Source Code Scanning, Data Loss Protection, Surveillance, Forensics
Regulatory and Compliance (RegTech): AML, Compliance Advisory, Rogue Trading Prevention, Automated Compliance Monitoring, KYC, Contract Due Diligence, Information Governance
Operations: Recruiting, Spend Analysis, Autonomous Documentation, Credit Risk, Automated Reporting, Invoice Processing, Vendor Management, IT Support and Infrastructure Management
Others: Employee Services, Expertise Network, Streamlined Mailing, Core Banking, Automated Scheduling
Cognitive Scale: Unbundling is driving AI
Practical AI: bringing scalable machine intelligence and continuous learning to the enterprise ROBERT GOLLADAY, COGNITIVESCALE presented that everything is unbundled and that is the main driver for AI.
Nvidia: Computer vision inside
Cutting-edge research teams, hyper-scale data centers, enterprises using AI…: deploying deep learning everywhere SERGE PALARIC, NVIDIA
Serge had a few excellent slides which covered a range of themes. For me, the significant ones are
a) The Nvidia platform scale from the Jetson (which we use) to DGX1
b) Edge processing for the Jetson . I discussed the theme of deploying a model to the Edge from the cloud in the article Continuous improvement IoT AI learning
c) The significance of Computer Vision in many areas including Drones
UBS: AI v.s Blockchain
Annika Schroder from UBS made a very interesting comment – for Banks, #blockchain is collaborative v.s. #AI is competitive – I never thought of it in that way. Interesting insight
UBS: AI capabilities stack
Also from UBS – an idea of the AI capabilities stack
Uber’s AI problem is likely to be replicated in many other industries
Uber had an interesting slide around unpredictable short term demand and a diverse range of suppliers (as the main driver for AI) . I think many more businesses would soon be in a similar situation and would need to deploy Enterprise AI
Typical Business Challenges
Higher customer service expectations
High number of products
More frequent shipments
Uber head of Deep learning believes in Strong ai
Uber’s Deep learning head believes in Strong AI in this lifetime! (machine’s intellectual capability is functionally = to human’s.) A strong statement indeed.
PWC virtual consultant collaborate double entry book keeping
“Deploying machine learning MICHAEL RENDELL, PWC” made three interesting comments
a) PWC is developing a virtual consultant who already ‘works i.e. the person can advice similar to an existing human PWC consultant
b) An AI was able to ‘learn’ deep learning over a weekend after being fed some million invoices(I do not recollect the exact number)
c) In the near future, we will have meetings with (say) 3 humans and an AI. This will be a real collaboration i.e. the AI will not be expected to just perform some tasks but people will be expected to actively collaborate with the AI
Offshoring: How many people for an outsourced/offshore project?
This is a personal observation. There are a lot of people who simply do not see AI for it’s disruptive potential. I had a conversation from a representative of a large IT offshoring company – who asked me ‘How many people would it take to typically develop an AI system?’ Contrast with PWC above! I think ICT outsourcing/offshoring companies will be the ones earliest hit by AI if they do not adapt!
Supernatural interfaces (snips) privacy by design
Privacy by design: a visionary way of using technology and data that respects confidentiality YANN LECHELLE, SNIPS Provided an example of Privacy by design being central to future services. examples include: Connected car: “Let’s refuel on the way at the usual gas station” Location and booking: “Find me the cheapest seafood restaurant near my hotel” etc
On a personal note
It was great to see long term friend from the mobile industry David Wood @dw2 . Also looking forward to reading @cccalum book t- also recommended by David The Economic Singularity: Artificial intelligence and the death of …
In conclusion, this was a great event and I very much hope it will be held in London again next year. The event reflects the rise of AI. There were many interesting insights also for my course Implementing Enterprise AI.
Link: #Enterprise #AI insights from the AI Europe (@AI_Europe) event in London