AI: Separating Hype From Reality
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Artificial intelligence technologies like machine learning, deep learning, virtual assistants, and conversational interfaces are surrounded by a lot of hype. The Gartner Hype Cycle is used to evaluate emerging technologies on a curve from inflated expectations to maturity. Real-world examples of AI include uses in marketing like Google ads, ecommerce like product recommendations, and logistics optimization. The future of AI will be shaped by factors like data availability and privacy concerns as well as each country's policies around developing and applying new AI technologies.
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AI: Separating Hype From Reality
- 1. AI: It's Not All Hype. But choose wisely. FRED MAUDE @NMPI_DIGITAL
- 2. What AI tech is surrounded by hype? What are the real world examples? What is the future for AI?
- 3. Source : Gartner Expectations Time Innovation Trigger Peak of Inflated Expectation Trough of Disillusionment Slope of Enlightenmen t Plateau of Productivity 1. Separating hype and commercial promise. 2. Reducing risks 3. Aligning business value with IT analysts Will support in… Gartner Hype Curve
- 4. Artificial General Intelligence Conversational User Interfaces Virtual Assistance Deep Learning Machine Learning Smart Robots Artificial General Intelligence Conversational AI Platform Deep Neural Network (ASICs) AI PaaS Smart Robots Deep Neural Nets (Deep Learning) Virtual Assistance Gartner Hype Curve 2017 Gartner Hype Curve 2018 Innovation Trigger Peak of Inflated Expectation Trough of Disillusionment Slope of Enlightenmen t Plateau of Productivity Expectations Time Source : (Gartner Hype Curve)
- 5. Non-mover Deep Learning Newbies ASICs AI PaaS Progressors Virtual Assistance CUIs Reversals Smart Robots Absentees Machine Learning
- 7. Non-mover Deep Learning Newbies ASICs AI PaaS Progressors Virtual Assistance CUIs Reversals Smart Robots Absentees Machine Learning
- 8. What AI tech is surrounded by hype? What are the real world examples?
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- 18. Universal Google Shopping Issues 1. Regularly changing stock leads to overreliance on the ‘Everything Else’ group 2. Flat bidding results in: a) Showing for old stock b) Showing the cheapest product c) Not showing for the top performing product 3. As there are no keywords, advertisers lack control over the search terms they show for, resulting in wasted ad spend.
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- 21. What AI tech is surrounded by hype? What are the real world examples? What is the future for AI?
- 22. DATA Deep Learning • Consumer data restrictions • Tech at odds w. Government AI Restrictions AI Drivers • Companies incentivised by government • Convenience > Privacy • Wealth of engineers “China will be the world’s dominant player in artificial intelligence by 2030.”WEST EAST
- 23. Ecommerce Brain Ali Assistant Store front personalisation Smart Supply chain Logistics
- 25. Any QUESTIONS? FRED MAUDE @NMPI_DIGITAL
Editor's Notes
- Hello – Feed Management & Google Shopping – Product Development & AI. Comparison Shopping Service. Talk Title Spoken about lots – Make it as useful as possible.
- Gartner Hype curve – Separate Hype from real commercially viable products/solutions Have a look at our industry and beyond – Using a Case Study Future of AI – and make some predictions of our role in AI. Which areas may reach full adoption.
- Explain what it is Each section relevance How it will support Value vs technical possibility
- 2017 – 1 technology in Innovation Rest in Peak of Inflated Expectations – Meaning : Lots of media coverage about success with limited failures Did anything change into 2018? The answer is not a lot Got to be careful/realistic Just want to have a look at the movements that were made, however slight they were – tell us a lot.
- Machine learning is an absentee – Data Analytics – That uses past data to decision which are ultimately fed… - IBM Chess computer. Omission is likely a result of focus shifting towards the subset of deep learning. Why?
- This cartoon explains a lot – “Read It” It is a joke, but has some important lessons in it. Machine learning, is still directed by humans – and will only act with the information prescribed in the way prescribed. Open to Human Bias. Amazon Robot.
- Deep learning – goes someway to solving this Use of neural nets (way brain works) – Framework for may different ML algorithms on top of each other. Include many aspects – and understand the relative importance Alpha Go / Jeapody – Computer beat out master. Take what it knew and apply it to a situation it has never been in
- Tech is surround by hype – promise in understanding downfalls of traditional ML, shift to Deep learning The development of new tech in ASICs (keep and eye) PaaS will open this up to all movement in VA and CUIs. So what for the real world examples?
- Noticed – All pored by this. Take a deeper look at what is available
- GCP – for the company – Connecting your companies data SciKit and Tensor flow – with limited coding – World Cup Predicting – Germany to the final (went out the groups) – Morocco to semis (god knows) Point is – it is there for anyone to use
- rankBrain now handles 100% of SQs Perhaps more interesting – Inception of Deep learning on SEO is fast expanding Self regulating system Scary thought to think we may not be entirely involved. What will happened? Facebook negotiating robots
- More Everyday used – VA – already testing LIA (come ask me) Image search – Mates jeans – And go to the shopping tab. Translate – Ad Copy & Landing Pages – Not only to human analysis, but CTR and CR Develop new phrases
- Google Marketing Platform Loads and Loads – But be careful? Bespoke automation or Machine learning. Target one KPI (ROAS, conv, clicks) What is your KPI, s their multiple You may need to set up your own automation. Don’t be lazy Our own automation to overcome a variety of unique and ubiqutis issues with google Shopping.
- This campaigns was for Harvey Nichols And has one awards that have outdone many complex ML
- On top of this industry challenge, a unique challenge presented nmpi and us an opportunity to enhance customer ad experience. Harvey Nichols partner with Pitney Bowes, a global technology provider, who I am sure you have heard of. Harvey Nichols utilise their platform to localise their product pricing and delivery costs outside of the UK and Ireland. This means that when you go to the Harvey Nichols site anywhere in the world, the product price and shipping is localised to your IP address. Let’s talk through exactly what that means…
- If you log in from the UK you will be shown the standard product rate. But log in from the US and the site makes a call to the Pitney Bowes API which recalculates the product price in-situ based on the live FX rate and your IP address. This allows Harvey Nichols to expand internationally showing local users prices that they can relate to. Of course this was a huge enhancement on-site user experience compared with showing GBP prices in all regions. However it does present difficulties when operating Localised Inventory, Google Shopping, dynamic social and dynamic display campaigns. As it means dynamic pricing cannot be easily inserted into our ads globally. For most of these forms of ads it simply means that it is not possible to show customers accurate pricing and then deep link to the corresponding currency. But, for Google shopping – it is a matter of operating shopping or not operating it.
- Lots of examples – Vas, CUI, Image Search – likely to impact performance marketers first At the moment you will have to combine these with clever automation of your own So what about our future?
- China will be the world’s dominant player in artificial intelligence by 2030. This isn’t a prediction by a researcher or academic, it’s government policy from Beijing. Data is the Fu