It’s hard to beat a lizard laden, sun shiny, ocean retreat like the Biltmore Hotel in Miami, but add in the Travel and Tourism Research Association (TTRA) conference and you’ve got my attention.
I quite enjoyed a number of the talks. Michael Rodenburgh from IPSOS Canada spoke about behavioural data and offered some fascinating tidbits about where people go to and come from during the tourism and travel customer decision journey. Passive behavioural data collection is a fabulous data collection tool and if you’re careful about obtaining explicit consent, I’m a big fan of it.
I was fascinated by a talk that Thomas Roth and David Paisley from Community Marketing and Insights gave about research with people who are LGBTQ+. Terminology seems to be in a permanent state of evolution and I never know what the most current respectful terms are. Needless to say, Tom and Dave will now be my go-to experts.
TTRA holds a number of academic tracks throughout the conference. In these tracks, graduate students and professors share their academic work which means there is a heavy contingent of highly trained, highly specialized researchers at the event. For those of you who love statistics and the nitty gritty of research details, these tracks are definitely for you. I love them for two reasons. First, of course, you learn about the research itself. But second, and most importantly for me, they are a great way to refresh your statistical and methodology training. ANOVA results take front stage and we see betas, f-values, p-values, and all the supporting statistics. People comment on and strategize over minute details. These discussions make me rethink what I thought I already knew and update my opinions about how to use statistics. Love it.
I was delighted to speak on the main stage Thursday morning about AI, chatbots, and voice search (my slides are below). I shared results from a Sklar Wilton & Associates white paper showing that the general population is fairly knowledgeable about the state of AI. AI can now write newspaper articles about anything you ask of it, AI can create humour that people actually laugh at, in some sense AI can even read your mind, and Google’s millions of dollars have allowed them to create an AI voice that is practically indistinguishable from the human voice. Of course, AI isn’t perfect and Joy Buolamwini of M.I.T.’s Media Lab has conducted research showing how facial recognition technology has trouble recognizing dark faces.
Technology for the regular folk who don’t have millions of research dollars backing us up has progressed to such a point where it is useful for customer service reps, marketers, and market researchers. Customers regularly use AI to book flights and hotels whether through chatbots on Facebook or voice assistants, we can now use AI moderators from companies like Quester to conduct surveys with anyone who has a voice assistant, and chatbots from companies like Elsient to conduct text surveys.
As fabulous as AI is, people are still unmatched for their ethics, emotions, and genuine caring for other people. This is what market researchers bring to the research table. Sure, we bring tech. Tech speeds things up and helps reduce technical errors. But people bring research results to life.
Oh, and if you’re wondering about the diversity of speakers, put your hands up, they’re playing our song, 54% of speakers were women. Rock on, TTRA!
Thank you Kathy and Scott for putting on a fabulous conference. We’re off to Melbourne Australia next year!
HP, Sony and Halo Neuroscience are making waves in the headset industry, with several news announcements in recent days. HP broke the news on Monday that they’ll adding to their core Z4 desktop line.The company is aiming to reach virtual reality (VR) creators with the introduction of Intel’s Core X series of processors, which is the most powerful processor available for consumers. The Core X’s processing power will help draw VR creators to the company’s line of desktops.
HP also announced their Windows Mixed Reality headset.The computer manufacturer will allow consumers to customize their desktop with the 18-core Core i9-7980XE. ECC memory is supported up to 256GB. There are also options to drop down to an eight-core processor if you don’t need all that power in a desktop.
HP has been quiet on the pricing of their desktops, but a starting price of $1,499 has been rumored for the cheapest models. Consumers hoping to buy their powerful PC from HP will need to wait until March to be able to add in the new customization options.
The company’s mixed reality headset is seeing the addition of a “pro version.” The version has washable face pads and also swappable face pads. The virtual reality headset will also be released in March.
HP claims their new headset is geared towards a professional environment, where users are interacting with the system for longer periods of time.The headset offers double padding options that add comfort to long span users. Outside of the added comfort and replaceable pads, the mixed reality headset is the same as the non-professional addition.
Sony has also announced that the company will be releasing their new edition of the Gold Wireless Headset. The headset is geared towards the gaming community, with compatibility for mobile devices and the PS VR.
Sony is following the same concept as HP, focusing primarily on comfort with their Gold headset. The headset is geared towards gamers that enjoy long gaming sessions. The headset focuses on comfort and performance, with 7.1 virtual surround sound options and hidden microphones.
There’s also a companion app which unlocks the headset’s power further. The app will allow for a more customized listening experience so that gamers can adjust how the headset sounds. Sony has not released further details on the Gold headset or where the headset will be available. The headset will likely be available at most retail outlets, but will not be available at online retailers, such as headsetplus.com, that focus on headsets for professionals rather than gamers.
Halo Neuroscience is taking a completely different stance to headsets with their brain-stimulating model. The company offers the Halo Sport neurostimulator headset, which is designed to help users improve muscle memory development and promote brain elasticity. The headset’s manufacturer just announced a $13 million Series B funding round, which will help fund the company’s future development.
Elastic foam nibs offer what the company calls a “Neuropriming” session. The company claims that the headset delivers pulses of energy into the brain’s motor cortex. Users are encouraged to wear the headset for 20 minutes a session, after which their brain will be better able to learn an activity.
Strength and endurance athletes are the target market of the company, which focuses on muscle memory. Musical learning is also benefitted after a session of neuropriming.
The company was founded in 2013 and was started with a group of neuroscientists interested in neurostimulation. Initial results show that wearers experienced better results than those that didn’t use the headset when comparing leg strength.The company’s headset is even being tested for how it can help stroke victims during rehabilitation.
Did you like this article? AI wrote it, not me! I think I’ll keep writing my own posts. http://articlecreator.fullcontentrss.com/go.php?
Voxpopme 7: How will automation impact the industry, and you personally, over the next twelve months?
Along with a group of market researchers from around the world, I was asked to participate in Voxpopme Perspectives – an initiative wherein insights industry experts share ideas about a variety of topics via video. You can read more about it here or watch the videos here. Viewers can then reach out over Twitter or upload their own video response. I’m more of a writer so you’ll catch me blogging rather than vlogging. 🙂
Episode 7: How will automation impact the industry, and you personally, over the next twelve months?
I’m not concerned with the next 12 months whatsoever. If we aren’t planning for the next five and ten years, we’re going to be in a lot of trouble. With that in mind, I’d like to consider how automation and artificial intelligence will impact me over that time frame.
The reality is that my job will change a lot. No longer will I receive a dataset, clean out poor quality data, run statistics, write a report, and prepare a presentation. Every aspect of that will be handled automatically and with artificial intelligence. I will receive a report at my desk that is perfectly written, with the perfect charts, and perfectly aligned to my clients’ needs.
So why will I still be there? I’ll be the person who points out the illogical outcomes of the data. How errors enter during the data collection process via human cognitive biases. I’ll be the person who interprets the data in an odd way that wasn’t predicted by the data but is still a plausible outcome. I’ll help clients read between the lines and use the results wisely rather than by the book – or rather, by the AI.
So how will automation and artificial intelligence impact our industry? If your business sells repetitive tasks, from survey programming to data cleaning to statistics to chart preparation and report writing, you’d better have a long term plan. Figure out your unique method of selling WISE applications. Not just data, but wiser data and wiser charts and wiser reports. There are already hundreds of companies innovating in these areas right now and they are waiting to find their customers. I expect you don’t want to hand over your customers to them.
The ideology of data by Sasha Gryjicic, @SashaG, Dentsu Aegis Network, #BigDataTO #BigData #AI #Intelligence
Notes from the #BigDataTO conference in Toronto
- Data marketing and artificial intelligence are headed in the wrong direction
- Marketing is the pursuit of convincing someone they need something, marketing is a commercial outcome to propel the broader economy forward, marketing uses media and communications to convince, largely based on human language
- Data is a digital expression of something in the world, organized and stored in many ways. We are finally getting the external world to use a single language but we can’t read this language. Humans don’t read binary code or extrapolations of code.
- Data violates the notion of scarcity and data is almost always out of both time and space context for necessity. Data is necessarily incomplete and it is not that thing itself. Data has inherent biases, is super messy, and contradictory
- We use data to optimize things that have already happened, or we generalize what we learn from data to engineer more of those outcomes, e.g., when managing an online store, we optimize data to get optimal business outcomes but this doesn’t help us learn why or what drives the decisions
- Intelligence is the ability to gather, category, organize inputs, store, reflect, and respond to them. For humans, intelligence is innate, structured, organized, and process oriented. We have a fixed capacity of intelligence and are creative with it. It is not the result of external stimulus.
- Language is the best way for humans to get access to our intelligence. It’s the language we use when we think. We talk to ourselves more than we talk to others.
- The AI we’re building is like automated statistics. We brute force relationships and create a black box of intelligence. We don’t understand why a computer makes certain decisions because we cannot hold enough variables in our mind to understand. Are algorithms intelligence or optimization? We are drifting further from understanding what intelligence really is. It’s not AI at all.
- We’re accelerating the fatigue of positive reinforcement. We’re following bad after bad. We’re heading away from language which is the only way to understand ourselves.
- Intelligence seems to include morality, the ability to store and reflect and take decisions based on reflections.
- We need to back away from disorganized data. We need to pause and relfect on what we see in the data to understand ourselves better. We need to dive into our own intelligence better. Reflecting on something is more important that acting on something.
Future of the smart home by Emily Taylor and Manish Nargas, IDC Canada, #BigDataTO #SmartHome #ConnectedHome #AI
Notes from the #BigDataTO conference in Toronto
- By 2020, every home will have 40 connected devices – TV, appliances, health, assistance, security
- Wearables help consumers track and log their activities such as wellness goals, athletic training, weight loss monitoring, medication reminders, gamification of activities. 1 in 5 Canadians currently own a device as a wristband or a watch and 70% of those owners have no plans to upgrade or replace. 60% of consumers are not interested in wearables at all. Designs will be less obvious, have improved battery life, and use new materials like smart fabrics. Medical devices will have better reliability and validity and this will help the healthcare sector and be relevant for insurance companies
- Security devices – smoke alarms, motion sensors, doorbells, security systems, remote home monitoring. These devices offer peace of mind. It’s no longer about emergency services but monitoring to see if the kids are home, a window is opened, the jewelry box is still there, perhaps even see if it’s a friend or foe at your front door.
- Home automation – these devices will help us reduce energy usage, increase safety including devices such as thermostats, light switches, outlets, appliances. IKEA has launched a smart home lighting system with wire-free lighting at a lower cost than their competitor. They will bring this technology into every piece of furniture and curtains [window blankets 🙂 ]
- Personal health devices – These devices will result in increased awareness of monitoring. Health monitoring will take place from the home not a hospital and will result in fewer trips to the doctor and hospital. Connected clothing will help with this. Gym equipment brands now sync with health monitoring devices so you can monitor treadmill and walking together and get more consistent results.
- Intelligent assistants/bots – more natural way to interact with machines, removes the complexity of interconnections, vocalizes thought and activity, uses real time machine learning. Low adoption rates in Canada but many bots aren’t available in Canada. Connecting a speaker to the internet isn’t revolutionary but it can improve personalization. 60% of Canadians don’t care about bots but bots are here to stay. It is Alexa and soon will be your butler. It will be ubiquitous.
- There are gaps. Many devices are siloed right now. They have limited conversations with other smart home devices. The market is too focused on DIY right now as people want to solve specific problems not do the entire home in one shot. There is little support across the solutions.
- Do you need a smart-fork that monitors how quickly you eat? Do you need this fork to connect to your lights and smoke alarm?
When will we drive autonomous vehicles, by Kashmir Zahid, Ericsson Digital Services (Great talk) #BigDataTO #BigData #AI #Automation
Notes from the #BigDataTO conference in Toronto
- 1996 GM introduced Onstar. It had a weak interface, few features, and was mainly designed to offer roadside assistance.
- 2010 saw in-car navigation but it still wasn’t user friendly nor easy to operate while you were driving.
- 2012 Tesla built an all electric car and people could finally see the possibilities of vehicles with electricity and connectivity. Now that vehicles had so much digital, manufacturers could no longer stay in the shadows and let dealerships handle all the consumer interactions.
- 2014 Apple CarPlay and android auto were introduced. Connectivity was embedded in the car from the time it was installed in the factory as opposed to being added by the consumer after the fact.
- 2015 remote diagnostics are now available, repairs can now be recommended by the vehicle rather than going to the dealership or following the manual.
- 2015 Tesla creates autopilot, a self guiding car but the user is still expected to take physical control when needed.
- 2017, the Google car is no longer a science project, it is a reality.
At CES, three trends were noted
1. cars will be integrated into your life and communicate with your personal device, e.g., your home will be ready to receive you when you arrive, the temperature is set appropriately, the lights are turned on, the garage door is opened, and the turkey is ready to be taken out of the oven
2. Automation will create a natural experience of talking to your car, Alexa is winning here [although it just accidentally bought Whole Foods so I don’t know about the quality at this point]
3. Car to car communication – this will allow vehicles to see and talk to each other, so they can maintain speed and safety among other cars on the road
- Now that everything talks to everything, our user experiences will be completely transformed.
- By 2020, 90% of cars will be connected
- 4 trends in the industry
1. Cars must be connected, software defined car
2. Electrification, ITS, infrastructure
3. Automation, connected automated mobility
4. New business models, multi industry ecosystems
- This is the largest change in transportation since Ford’s model T
- Soon, we will have everything we need to travel but we won’t own the car. [Think of music, we no longer own the music we buy and we could lose it instantly if Apple decides to shut something down]
- Insurance will depend on how you drive, your telemetrics. And later on, insurance won’t be necessary as human drivers won’t be responsible for safety.
- Emergency assistance providers will be affected as cars will have embedded systems that alert first responders instantly to ask if you are safe.
- Government providers will need to reconsider what legislation is needed to take care of drivers and roads.
- 13 out of 14 of the big vehicle manufactures plan to make an autonomous vehicle in the next couple of years
- Google, Apple, Intel, Microsoft and Amazon have focus and investment in self driving car projects. Telecom operators like AT&T, verizon, Vodafone see the potential of new revenue in self driving car. Uber, Lyft, DIDI and many other startups are trying to disrupt the traditional car ownership model.
- The passenger economy will be worth $7 trillion by 2050.
- We are about to see consumer mobility as a service – one stop shop for transportation for everyone who doesn’t own a car [this is amazing for people who don’t know how to drive, are too old to drive, too young to drive, not well enough to drive]
- This will save over half a million lives due to safety from fewer accidents. And, it will free up your time since you don’t have to physically drive.
- We are two years away from letting people sleep in a Tesla on long road trips where the car has not made the trip before – Elon Musk
1. Public safety – people need to trust the machine to work while they sleep.
2. Data privacy and security – who has, uses, and sells my data. It’s not transparent right now.
3. Rules and Regulations – Who is liable for an accident? Who owns the vehicle that caused the accident?
- Connected cars will open multiple innovative services.
- They will improve the efficiency and security of new value added services for both consumers and enterprises.
I’ll admit I didn’t have high hopes. How good could a free conference about big data and artificial intelligence be? Especially if the upgrade tickets, which I so frugally declined, were only $75? Well, I was pleasantly surprised.
Let’s deal with the negatives first. The morning registration line was long and it took some people 30 minutes o get through it. The exhibition hall was small with not nearly as many vendors as I am used to seeing at conferences. There was no free wifi in the main hall (um, admission was free so why do we deserve free wifi too?). Sometimes the sessions were so packed, there wasn’t even room to stand. And, some speakers didn’t even show up because, well, airplanes.
However, those negatives were completely washed aside with the positives. Some of the talks were quite good. Some of the speakers were quite good. The topics were quite good. They gave out free conference programs. And did I say free? Some free things are worth what you paid for them. This one was worth a lot more. I highly advise you to go and it’s definitely on my 2018 conference schedule as time well spent.
- Data science is often handled at the tail-end of a project. We only take the time to learn what happened after the fact and when it’s too late to do anything about the current situation. We need to do a better job of using our data for the future – for segmentation, targeting, to understand what our customers want, to uncover blind spots.
- Good data scientists care where the data came from, who created it, what it represents. They don’t just take the data and run it through stats programs and spit out reports. It’s not just about statistics and reporting. Data quality must come first.
- The real money is not in having the data but rather in knowing what questions to ask. Literally everyone has data but only the companies that hire the smart brains to ask the right questions will succeed with big data.
- You might think using artificial intelligence is very impersonal. On the contrary. It’s impossible for a human being to be personal with hundreds and thousands of people but AI allows you to be far MORE personal with thousands or millions of people.
- Computers and artificial intelligence need to learn the senses – for instance, they need to learn to see the types of moles on skin that will become cancer, learn to hear which wheels on a train are cracked and about to cause a train wreck.
- Algorithms are what make computer see and listen and as such algorithms are the future. Soon, companies will brag about their algorithms not their data.
- We need to let computers do the pattern recognition so that humans can do the strategizing and reasoning
- If you want to work with big data but can’t afford it, have no fears. So much software is free and open source. You can do anything you want with free tools so don’t let dollars hold you back from doing or learning.
- The danger with artificial intelligence is training it with bad, untrustworthy, biased data. We’ve all seen the reports of AI perpetuating racism because the training data contained racist data. You must choose good datasets that are clean and genuinely unbiased and only then will you find success.