You probably want to avoid any class that teaches you to think in an impersonal, linear, generalized kind of way — the kind of thinking A.I. will crush you at. On the other hand, you probably want to gravitate toward any class, in the sciences or the humanities, that will help you develop the following distinctly human skills:
A distinct personal voice. A.I. often churns out the kind of impersonal bureaucratic prose that is found in corporate communications or academic journals. You’ll want to develop a voice as distinct as those of George Orwell, Joan Didion, Tom Wolfe and James Baldwin, so take classes in which you are reading distinctive and flamboyant voices so you can craft your own.
Presentation skills. “The prior generation of information technology favored the introverts, whereas the new A.I. bots are more likely to favor the extroverts,” the George Mason University economist Tyler Cowen writes. “You will need to be showing off all the time that you are more than ‘one of them.’” The ability to create and give a good speech, connect with an audience, and organize fun and productive gatherings seem like a suite of skills that A.I. will not replicate.
A childlike talent for creativity. “When you interact for a while with a system like GPT-3, you notice that it tends to veer from the banal to the completely nonsensical,” Alison Gopnik, famed for her studies on the minds of children, observes. “Somehow children find the creative sweet spot between the obvious and the crazy.” Children, she argues, don’t just imitate or passively absorb data; they explore, and they create innovative theories and imaginative stories to explain the world. You want to take classes — whether they are about coding or painting — that unleash your creativity, that give you a chance to exercise and hone your imaginative powers.
Unusual worldviews. A.I. can be just a text-prediction machine. A.I. is good at predicting what word should come next, so you want to be really good at being unpredictable, departing from the conventional. Stock your mind with worldviews from faraway times, unusual people and unfamiliar places: Epicureanism, Stoicism, Thomism, Taoism, etc. People with contrarian mentalities and idiosyncratic worldviews will be valuable in an age when conventional thinking is turbo powered.
Empathy. Machine thinking is great for understanding the behavioral patterns across populations. It is not great for understanding the unique individual right in front of you. If you want to be able to do this, good humanities classes are really useful. By studying literature, drama, biography and history, you learn about what goes on in the minds of other people. If you can understand another person’s perspective, you have a more valuable skill than the skill possessed by some machine vacuuming up vast masses of data about no one in particular.
Situational Awareness. A person with this skill has a feel for the unique contours of the situation she is in the middle of. She has an intuitive awareness of when to follow the rules and when to break the rules, a feel for the flow of events, a special sensitivity, not necessarily conscious, for how fast to move and what decisions to take that will prevent her from crashing on the rocks. This sensitivity flows from experience, historical knowledge, humility in the face of uncertainty, and having led a reflective and interesting life. It is a kind of knowledge held in the body as well as the brain.
The best teachers teach themselves. When I think back on my own best teachers, I generally don’t remember what was on the curriculum, but rather who they were. Whether the subject of the course was in the sciences or in the humanities, I remember how these teachers modeled a passion for knowledge, a funny and dynamic way of connecting with students. They also modeled a set of moral virtues — how to be rigorous with evidence, how to admit error, how to coach students as they make their own discoveries. I remember how I admired them and wanted to be like them. That’s a kind of knowledge you’ll never get from a bot.
And that’s my hope for the age of A.I. — that it forces us to more clearly distinguish the knowledge that is useful information from the humanistic knowledge that leaves people wiser and transformed.
Generative AI models – computer programs that create text, images, code and other forms of content based on text-based prompts – entered mainstream consciousness only recently. But many of its most well-known models have already become immensely popular; it only took five days for ChatGPT, an AI model launched in November that generates text, to reach a million registered users.
Generative AI has become a red-hot topic in adland, presenting both vast new creative possibilities and serious ethical dilemmas. Given the newness and the complexity of the new technology – as well as the considerable amount of hype that has begun to circulate around it – many marketers are likely asking themselves questions such as: ‘Is this a fad?’ ‘Do I need to teach myself how to use these tools?’ ‘To what degree will it reshape my job and my industry?’
We asked marketing experts and ChatGPT to chime in about the current and potential future impacts of generative AI upon the marketing industry:
ChatGPT (responding to the prompt ‘Write 200 words or less about how ChatGPT and other generative AI models like Midjourney are transforming the marketing industry’): “ChatGPT and other generative AI models like Midjourney are transforming the marketing industry by enabling more personalized and efficient communication with customers. These models can be used to generate personalized email and social media content, as well as to assist with customer service interactions. They can also be used to create targeted marketing campaigns by analyzing customer data and identifying patterns and trends.
“Generative AI models can also help with content creation for websites and social media platforms, by writing product descriptions, blog posts and other types of content. Additionally, they can analyze large amounts of data, such as customer reviews and feedback, to identify common issues and concerns, which can help companies improve their products and services.
“Furthermore, AI models can automate repetitive tasks, such as data entry, and free up human workers to focus on more complex and creative tasks. This can lead to cost savings and increased productivity for companies.
“In short, ChatGPT and other generative AI models are helping companies to communicate more effectively with customers, create more personalized and effective marketing campaigns, and improve the overall customer experience. This is the most promising way to increase the efficiency of customer engagement and lead generation.”
John Mescall, global executive creative director, McCann Worldgroup: “I can absolutely envision a world in which the majority of marketing is created by generative AI – and I’m sure there’ll be nothing horribly wrong with the work. The technology will be good enough to do a decent job of mimicking standard human creativity.
“But the very best and most effective work will still be a product of human intelligence, artistry and creativity. Because when everyone has access to the same technology, the only way to truly create something unique will be to employ human minds that are capable of seeing, thinking and strategizing beyond the norms.
“I predict that technology will eventually replace all but the most creative and nuanced of tasks. Hopefully, this is a nudge to us all. Creativity has always been – and always will be – our most essential currency.”
James Huerta, executive director of creative technology, Havas CX: “2022 was the year that creative applications of AI went mainstream. Even your luddite uncle has read about ChatGPT and knows it by name or has played with Lensa AI and wants to show you his movie posters. There are two good reasons for this shift: first, these systems are finally capable of generating words, imagery and art that feel credibly human and not like a canned Q&A bot or someone’s dystopian nightmare; second, they are widely accessible on the web and apps with intuitive interfaces that make playing with AI and seeing immediate results – something that almost anyone can do.
“Within a marketing context, these advances deliver a lot of power for brands to be able to generate large amounts of personalized messaging and content. As more of these services are available on the web via application programming interfaces (APIs), embedding ‘AI-on-the-fly’ could become as easy as setting up a Squarespace site.
“But let’s not get ahead of ourselves. The biggest challenges around ethics, attribution and, most importantly, facts have not been solved. We’re currently in the stage of what could be called ‘plausible content,’ which looks great at a glance, but if the system is inventing facts or images, we’ve created something dangerous. Humans will need to supervise this closely for the foreseeable future and remain accountable for its impacts. And for an industry like ours, I’d suggest that creative work that is ‘plausibly good’ to the untrained eye creates more potential harm than work that is obviously bad.”
Mansoor Basha, chief technology officer, Stagwell Marketing Cloud: “Artificial intelligence and machine learning are at the forefront of digital transformation across industries and will undoubtedly remain there for the foreseeable future. In a 2011 op-ed, Marc Andreessen observed an environment in which software was increasingly becoming king, famously stating that ‘software is eating the world.’ His observation came about a decade after the peak of the 1990s dot-com bubble as companies like Facebook and Skype were booming. Looking to the next decade, I believe that AI and machine learning will be eating the world, changing the way we work, live and interact with brands.
“I predict that as AI technology continues to change everything around us, consumers will have more time to be engaged through new forms of media. This will give brands the opportunity to leverage more pointed advertising to reach their audiences. AI will find brands’ ideal consumers and reach them in the right place at the right time, especially as augmented reality and virtual reality inch further into mainstream culture. And as the hype around generative AI simmers down, marketing teams will become more comfortable adopting a wide range of AI tools that can help them build powerful workflows that drive innovation, aid in decision making and create new business models.
“Overall, generative AI will be an entry point for many marketing teams as they look for relevant ways to use new technologies in their day-to-day work. The future of marketing will be more precise and more focused, and it will see much higher levels of engagement.”
Aaron Kwittken, founder and chief executive officer, PRophet: “Generative AI, while not perfect, is the needle that pierced the veil of doubt and fear among marketers when it comes to adopting AI technology. The current limitations are only the result of a lack of data.
“When paired with the right inputs, this technology will make marketers more efficient by enabling them to create base content faster and better, and freeing them up for higher value tasks like editing and strategy deployment. In addition to assisting with content creation for press releases, social posts, pitches, marketing collateral, blogs and other text-based materials, this technology can be a huge aid with legal and compliance issues, especially when working with third parties like influencers and celebrity spokespeople.
“But make no mistake – the downsides will need to be managed.
“Generative AI may reduce the need for some junior staff members; it could be used to create and spread misinformation and disinformation; and it could make professionals more complacent, less creative and more transactional. The responsibility will fall on marketers to figure out how they’ll use this new technology to enhance their current activities, rather than simply replacing them.”
Ben Williams, global chief creative experience officer, TBWA Worldwide: “Generative AI models are making their way into mainstream creative processes as valid and powerful tools to fast-track the creation of imagery, text, videos and other assets. Its real power is accelerating the manifestation of a thought or an idea – a radical departure from when visualizing or writing a concept could take hours or even days.
“But while these tools are helpful, human involvement in the creative process is still essential. These tools are exactly that: tools. As they develop we’ll still need humans to apply their discerning eyes and decide what works from a creative perspective. We need humans to identify and flag any biases or falsities in the content the AI models create. And when we’re using these tools to create content for a brand, we need humans to ensure that such content aligns with that brand’s broader values and messaging.
“We need to tap into the power of these tools to explore the possibilities and supercharge creativity across the board. Let’s just remember that we, as humans, are still very much needed.”
If you’re anything like me, there are two questions on your mind as you enter a bike race:
How well will I do today?This is the personal expectation piece. Do I anticipate a shot at the podium? Or will I be getting dropped at some point for some reason, and this is more of a workout or team effort than a win attempt?
Who are the strongest riders in this race?If I’m contesting the finish or working for a teammate, who are the key riders I need to be watching? If a weak rider attacks I don’t need to waste my watts, but if a strong one does, I may want to respond!
Back in early 2021, one Zwifter created ZRace – an app that answers both of these questions with impressive accuracy. The app predicts the finishing places of riders signed up for Zwift races, and according to its creator, the tool’s Top 5 prediction is quite accurate, with a 95% probability that 3 predicted top 5 athletes will indeed finish in the top 5.
How It Started
In early 2021, Bruno Gregory had already created racedata.bike, an app that analyzes and predicts races across all categories of cycling in the US. Then Covid happened, sparking Bruno’s interest in Zwift and his subsequent participation in Zwift races.
He quickly learned there was a wealth of Zwift racer data available: power, heart rate, weight, age, sex, historical results, and more. And he realized that, given this additional data, analysis and predictions could be made much more accurate than the initial version of his app.
The “Random Forest” decision tree algorithm is used in the machine learning which powers ZRace
I won’t go into detail how exactly ZRace calculates its predictions, because those details are above my pay grade. But it uses machine learning (a form of AI), and the more races that happen, the more accurate it gets. (To read how the project unfolded, including Bruno’s iterative approach to selecting the best predictive models, read his post on Medium.com.)
What It Does
Bruno describes ZRace like this:
ZRace analyzes all athletes registered in a race and predicts possible winners. It also analyzes each category and presents the average power required for you to have a good result. In addition, athletes with specific profiles are identified, such as climber, sprinter, and time-trialist. This way, depending on the race’s course, it is possible to predict who will have a better result or even who you should keep an eye on for a certain part of the race.
Let’s dig into each of those features, which all live on the Race Predictor screen.
The Race Predictor
While many Zwifters simply visit ZwiftPower and sort the signup list by rank to find out who the top riders are, the ZRace Race Predictor is much more precise, using multiple variables plus a robust machine-learning algorithm to predict each rider’s finishing position.
From the ZRace.bike homepage, select any race. This will load up the Race Predictor for that event. In a multi-category event it defaults to showing the A category predictions, but you can select the category you’d like to view from the “Category” dropdown. Here’s the Race Predictor screen for an upcoming KISS Race:
Along the top you have a summary of each category’s signup list, including the number of riders signed up and the FTP average of the field.
You also have top riders selected by profile: a top sprinter, climber, and break away rider. Depending on the route profile and race situation, these would be good riders to watch.
List of Past Races
Curious how accurate ZRace’s predictions are? Click “Past Races on Zwift” on the left, choose a race, then click Results to see actual results and ZRace’s prediction.
Predict Me
Click Predict Me, select your race category, and enter your Zwift ID. The app will predict your result in the next hour’s Zwift races. (Not sure how to find your Zwift ID?) Here’s what it predicted for me, entering the B category:
Race Statistics
This portion of the ZRace app is quite interesting. It displays stats for:
Most popular days of the week and time to race
Winners by country
Most popular race events
Most popular race routes
Toughest races (based on power numbers)
Winner profile of men and women in all categories
Winners Ranking (top-ranked riders in each category based on ZRace’s algorithm)
A Few Gotchas
ZRace only lists ZwiftPower-registered riders, so it’s possible you could enter a race and get beaten by someone who hasn’t signed up for ZwiftPower. Then it’s up to you to wrestle with that age-old Zwifter question… if they aren’t on ZwiftPower, did they actually win?
The ZRace algorithm works well for iTT races and standard scratch races, but doesn’t work for handicap (chase) races. It also can’t predict the winner of a points race with intermediate point segments, since it is only predicting the finish order.
The system can take a bit of time to make its prediction, because it has to process rider data when you view an event. Be patient, it’s worth it!
Artificial intelligence is beginning to be usefully deployed in almost every industry from customer call centers and finance to drug research. Yet the field is also plagued by relentless hype, opaque jargon and esoteric technology making it difficult for outsiders identify the most interesting companies.
To cut through the spin, Forbes partnered with venture firms Sequoia Capital and Meritech Capital to create our second annual AI 50, a list of private, U.S.-based companies that are using artificial intelligence in meaningful business-oriented ways. To be included, companies had to be privately-held and focused on techniques like machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to “understand” written or spoken language), or computer vision (which relates to how machines “see”).
The list was compiled through a submission process open to any AI company in the U.S. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). In total, Forbes received about 400 entries. From there, our VC partners applied an algorithm to identify the 100 with the highest quantitative scores and then a panel of eight expert AI judges identified the 50 most compelling companies. Read the full profile on Forbes: https://www.forbes.com/sites/alanohns…
L’US Census Bureau, un organisme délivrant des études de marché basées sur l’analyse de données, publiait le 16 juillet dernier un rapport sur l’utilisation de l’intelligence artificielle en entreprise. Une enquête effectuée fin 2018 auprès de 583 000 entreprises américaine qui met en avant une adoption très résiduelle.
L’intelligence artificielle, encore en phase de déploiement précoce
Le machine learning n’est par exemple déployé que chez 2.8% des entreprises sondées. Si l’on ajoute les autres pans de l’intelligence artificielle considérés dans l’étude (reconnaissance vocale, véhicules autonomes, machine vision, robotique, RFID Réalité Augmentée, etc), nous tombons sur seulement moins d’une entreprise américaine sur 10 (8.9%) ayant recours à au moins un d’entre eux.
Un chiffre qui a de quoi surprendre, mais qui est à mettre en perspective. L’étude date tout d’abord de fin 2018. Nul doute qu’avec l’accélération exponentielle des usages autour de l’IA ces six derniers mois, les résultats de l’enquête sont un instantané en décalage avec la réalité du monde post-Covid-19. Comme nous vous en parlions récemment, d’ici 2022, les technologies basées sur l’intelligence artificielle devraient faire leur trou dans 80% des entreprises.
Mais alors, comment expliquer un gap si conséquent entre l’état des lieux à fin 2018, et la projection à quatre années plus tard ? “Nous n’en sommes qu’au tout début de l’adoption de l’IA. Les gens ne doivent pas penser que la révolution de l’apprentissage machine est en train de s’essouffler ou qu’elle est du passé. Il y a un raz-de-marée devant nous” présente Brynjolfsson, directeur du Standord Digital Economy Lab et co-auteur de l’enquête de l’US Census Bureau.
Une vague d’adoption anticipée, mais encore prématurée si on se réfère aux chiffres de l’enquête. Chiffres bien en-dessous de ceux publiés à la même époque par deux autres enquêtes pilotées par McKinsey et PwC. La première, sortie en Novembre 2018 avançait le chiffre de 30% de dirigeants exploitant l’intelligence artificielle sous une forme ou une autre. La seconde, de PwC, paraissait fin 2018 et montrait qu’un dirigeant sur 5 planifiait le lancement d’une technologie liée à l’intelligence artificielle en 2019.
Capture d’écran : Wired
Les grandes entreprises, leaders sur l’adoption de l’IA
Eric Brynjolfsson explique que contrairement aux études de leurs confrères, celle de l’US Census Bureau se veut plus représentative du tissu économique américain, car non-focalisée sur les Fortune 500. Une méthodologie qui se retranscrit dans la lecture d’une double réalité au niveau des entreprises : presque 25% de celles de plus de 250 employés ont investi dans une forme d’intelligence artificielle, quand seulement 7.7% des entreprises de moins de 10 employés ont fait de même.
“Les grandes entreprises adoptent”, dit Brynjolfsson, “mais la plupart des entreprises américaines – la pizzeria de Joe, le pressing, la petite entreprise de fabrication – n’en sont pas encore là”. Pour les grandes entreprises, ce rôle de porte-étendard dans l’adoption de ces nouvelles technologies sera déterminant dans le rebond économique. Portant en effet une proportion plus large de l’activité économique, il sera vital de voir ces leaders montrer la voie de la transition technologique.
Si la mise en place de composants d’intelligence artificielle peut paraître plus lente à enclencher chez des poids lourds du marché, la recherche de compétences en IA montre que la transition est lancée. Chez Google, le nombre de téléchargement de Tensorflow, son framework dédié à la création de programmes IA, a dépassé les 10 millions rien que sur le mois de Mai 2020.
Côté formation aux compétences de demain, Microsoft s’est allié au Français OpenClassrooms pour créer un programme “AI Engineer”, qui doit recruter et former 1000 ingénieurs en machine-learning et intelligence artificielle. “Nous unissons nos forces pour combler le fossé des compétences numériques en offrant l’expertise et le contenu de Microsoft en matière d’IA, de cloud computing, d’apprentissage automatique et de sciences des données à des étudiants de tous âges et de tous horizons via la plateforme d’enseignement en ligne interactive et de haute qualité d’OpenClassroom”, déclare Ed Steidl, directeur des partenariats avec la main-d’œuvre chez Microsoft.
Les projets intégrant de l’IA se multiplient
Les initiatives mettant en avant l’intelligence artificielle commencent également à pointer le bout de leur nez. De nouveaux terrains de jeu émergent, et pas toujours là où on les soupçonne. En avril dernier, nous vous parlions du projet d’Intel, baptisé CORail. Chargé de collecter les données sur les récifs coralliens affectés par le réchauffement climatique, la solution est entièrement basée sur l’intelligence artificielle. Le mois dernier, nous vous présentions Tuna Scope, application dédiée à l’évaluation de la qualité d’un thon à partir d’une simple photographie. Construite à partir du machine-learning, l’application est déjà exploitée par une chaîne de restaurants japonaise. Côté boisson, il y a par exemple AB InBev. À partir de données recueillies dans une brasserie dans le New Jersey, la société a mis au point un algorithme d’IA pour prévoir les problèmes potentiels du processus de filtration utilisé pour éliminer les impuretés de la bière.
Le “raz-de marée” évoqué par Eric Brynjolfsson est donc encore à un stade précoce. Le sujet de l’intelligence artificielle se démocratise toutefois à une vitesse exponentielle, et il faudra garder un œil sur la multiplication des initiatives dans les mois et années à venir.
Analytics is the discovery, interpretation, and communication of meaningful patterns in data; and the process of applying those patterns towards effective decision making. In other words, analytics can be understood as the connective tissue between data and effective decision making, within an organization. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.
Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.
2. Future of Data Science
AISOMA – Future of Data Science
Sebastian Raschka, researcher of applied Machine Learning and Deep Learning at Michigan State University, thinks that the future of Data Science does not indicate machines taking over humans, but rather human data professionals embracing open-source technologies.
It is common understanding that future Data Science projects, thanks to advanced tools, will scale to new heights where more human experts will be required to handle highly complex tasks very efficiently. However, according to McKinsey Global Institute (MGI), the next decade will witness a sharp shortage of around 250,000 Data Scientists in the U.S. alone. The question is whether machines can ever enable seamless collaboration between technologies, tools, processes, and end users. Automated tools and assistants can aid the human mind to accomplish tasks more quickly and accurately, but machines cannot ever be expected to substitute for human thinking. The core of problem-solving is intellectual thinking, which no machine, no matter how sophisticated it is, can replicate. (further information)
The ethics of artificial intelligence is the part of the ethics of technology specific to robots and other artificially intelligent beings. It is typically[citation needed] divided into roboethics, a concern with the moral behavior of humans as they design, construct, use and treat artificially intelligent beings, and machine ethics, which is concerned with the moral behavior of artificial moral agents (AMAs). (more info)
Artificial Intelligence quote
8. NLP / NLU Technology Stack
AISOMA – NLP Technology Stack
Natural language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation.(more info)
1. Wearables went mainstream, but AR glasses like Google Glass and Snap Spectacles aren’t as big a deal as tech companies thought they’d be.
In 2009, futurist Ross Dawson predicted that in the coming decade we could see “augmented humans” with AR glasses or contacts allowing us to control machines. Instead, Google Glass and Snap Spectacles both made Business Insider’s list of “Worst Tech of the Decade.”
Something about the tech just didn’t resonate with people, beyond the few superfans who tried them. Shortly after Google Glass was released, Google even had to warn wearers not to be “creepy or rude (aka, a ‘Glasshole’).” The company ended consumer sales of Glass in 2015.
2. Augmented reality, in general, isn’t as advanced as experts predicted 10 years ago. Breakouts like Pokémon Go were big hits, but other consumer products haven’t gone far.
Futurist Gerd Leonhard predicted that tablets would usher in an era of augmented reality’s dominance, which would be a “huge boon” to content production. AR has allowed for fun Snapchat effects and games like Pokemon Go, but it hasn’t changed daily life in the way that people thought it would — at least not yet.
3. Self-driving cars have gotten more advanced, but they’re not about to take over the roads anytime soon.
Autonomous vehicle technology from companies like Tesla has definitely improved, and reports of drivers falling asleep at the wheel have mostly been without injuries as the cars were able to compensate. But Tesla still says that autopilot mode requires “active driver supervision,” and a Tesla in autopilot mode earlier this month crashed into a police car, proving that the system is far from perfect. Self-driving tech from Alphabet and Uber have also yet to see a wide launch, and largely remain in the testing phase.
4. Cryptocurrencies like bitcoin were supposed to be the future, but they haven’t been widely adopted, and Facebook’s Libra currency has caused headaches for the company.
The last decade has seen plenty of highs and lows for cryptocurrencies. Investing in bitcoin early could have made you very wealthy by now, but many analysts see it as a bubble or niche financial product.
Cryptocurrency exchanges have been hacked, sometimes leading to investors losing their holdings. Some early investors have made millions, others trying to get in on the craze have seen their investments fall to a fraction of their value as crypto prices fluctuate wildly.
Facebook is working on the launch of its Libra cryptocurrency, and CEO Mark Zuckerbergtestified on the subject before the House Financial Services Committee in October. Lawmakers have been critical of the project, and many major backers including PayPal and Visa have dropped out.
5. Some scientists and researchers predicted that artificial intelligence would help us avoid human shortcomings, like bias, but while the technology shows promise, it’s far from perfect.
Incorporating AI into sectors like policing was predicted to to help us avoid prejudice, but even as AI plays an increasingly important role in daily tasks, bias among AI exists. In fact, researchers keep finding evidence that AI is far from perfect and can introduce similar biases as those held by people. From an AI algorithm that kept black patients from getting the same quality of medical care as white patients, to hiring algorithms that learned to prefer male candidates, it’s clearly early days for the technology.
Pour Jean-Philippe Blerot, à la tête des projets digitaux chez Carrefour Belgique, « un partenariat avec un géant du numérique comme Google était nécessaire pour le groupe ».
Carrefour propose, en partenariat avec le géant du web, un sommelier virtuel pour aider ses clients à choisir leurs vins. De quoi inaugurer l’arrivée de l’intelligence artificielle dans vos supermarchés.
Vous en avez déjà rêvé : mettre les pieds sous la table exclusive d’un restaurant 3 étoiles, vous faire bichonner par les équipes raffinées de l’établissement. De Karmeliet faisait encore récemment partie des quelques rares maisons belges à avoir reçu la haute distinction du Michelin. Benoît Couderé y a apporté son expertise de sommelier du pays durant plusieurs saisons. Avant de rejoindre le « retailer » Carrefour il y a deux ans.
L’idée ? Remodeler la cave des hypermarchés et la rendre plus accessible au commun des mortels que nous sommes. Alors quand les équipes digitales de l’entreprise lui ont proposé de se faire « disrupter », le maître aurait, paraît-il, accepter sans broncher.
Son savoir-faire est désormais intégré – par l’implémentation, d’abord, d’un riche fichier excell qui accorde mets et vins – à l’intelligence artificielle du géant de la Silicon Valley, l’Assistant Google. « L’idée est d’apporter l’élite à la table de chacun. Vous demandez : OK Google, je mange du lièvre ce soir et l’assistant vous propose en réponse un panel de vins (de la cave Carrefour, bien entendu, NDLR) qui s’accordent parfaitement avec votre dîner », explique Jean-Philippe Blerot, à la tête des projets digitaux et de l’e-commerce chez Carrefour Belgique.
En pratique, le « Sommelier Benoît » est aujourd’hui accessible en test dans trois magasins du groupe (Evere, Herstal et Zemst). À terme, une fois l’intelligence artificielle enrichie – « nous allons récolter les infos que les clients nous donnent à ce niveau, il s’agit d’une version bêta, loin d’être parfaite » –, le but est bien de l’intégrer aux applications de Carrefour. Et de décupler le principe à d’autres types de produits.
Offre plus personnalisée
Ce gadget sympathique peut sembler à première vue anecdotique. Il ne l’est pas. Google et Carrefour, c’est une histoire qui a débuté en juin 2018 et qui est faite pour durer, scellée par un partenariat à l’échelle mondiale. Chaque caddy rempli de produits par vos soins contient également une quantité de données personnelles impressionnantes. Or les grands « retailers » sont moins habiles que les Gaffa pour les exploiter et font face désormais à une concurrence féroce sur leur segment : Alibaba en Chine, Amazon ailleurs (notamment avec son service Pantry). « Il y avait une nécessité au niveau du groupe de s’allier à un géant du numérique », acquiesce Jean-Philippe Blerot. Preuve que le secteur de la distribution, sous pression, s’apprête à changer.
Intelligence artificielle (IA) et « machine learning » (soit les services « Cloud » de Google) permettent une multitude d’optimisations d’un business model donné : de la gestion des stocks en magasin à l’analyse de l’effet de la présence d’un concurrent, en passant par la traçabilité des produits jusqu’à, but ultime, la hausse du chiffre d’affaires.
Faire entrer l’IA en magasin et sur les applications du groupe, c’est la garantie d’une offre plus personnalisée pour (re)fidéliser un client qui éparpille désormais son pouvoir d’achat et se rend moins souvent dans des supermarchés géants. Influencer son parcours de courses aussi, s’il a tout de même fait le déplacement. « L’Assistant pourrait aider le client à mieux manger. En lui proposant de remplacer certains produits par des options plus saines sur base de son panier de courses habituel. L’idée générale est bien de personnaliser l’offre mais aussi d’encourager à la découverte », poursuit le responsable.
Des données « exclusives »
La question du respect de la vie privée est, bien sûr, ici, centrale. L’Assistant Google, qui touche 2 milliards de personnes dans le monde, est un écosystème à visée commerciale. La plateforme loue ses services à des tiers qui peuvent y développer des applications adaptées à leurs besoins.
Quid alors de la circulation, de l’utilisation et de la monétisation de vos comportements, « vinicoles » dans le cas présent. « Le « Sommelier Benoît » a été développé dans un environnement isolé, réservé à Carrefour. Aucune donnée n’est conservée par Google, nous ne gardons que le dialogue avec la machine », assure le responsable de projets. Pas question donc de retrouver sur le moteur de recherche des publicités connexes à vos demandes faites au sommelier (par définition, une telle extension serait contre-productive pour le partenaire). Chez Carrefour, on précise d’ailleurs « que si l’Assistant est intégré aux applications du groupe, une autorisation préalable sera toujours demandée aux clients avant d’utiliser leurs données. »
De quoi vous garantir un repas de Noël rehaussé de quelques grands crus du meilleur effet. Pour le plus grand bonheur de vos proches. Et également de votre « retailer » préféré…
The past decade has already forced a shift in the professional skills required of workers. New technologies like collaboration apps and document and knowledge capture tools have had a wide-ranging impact on what people can do: speeding up communication, enabling faster access to and dissemination of information, and multiplying reach. Yet even among all this progress, nothing promises to be more disruptive to the future of work than the introduction of artificial intelligence.
Recent data from McKinsey suggests that almost every occupation will be touched by automation. But the firm forecasts that intelligent technology is likely to automate away just 5% of roles, meaning that most of us will live in a world where AI helps us by taking on just part of our current jobs. McKinsey thinks that most occupations will experience around 30% of their tasks being ultimately automated. Most of us will find our roles changing, and we will find ourselves working alongside virtual colleagues. Having been on the front lines of this revolution for some time now, it is my belief that for the foreseeable future, technology will continue to take on tasks, not entire jobs. Change is hard for people, but I find that getting people to see this truth and learn to trust these new kinds of intelligent machines opens the door to remarkable transformations.
Marketing is an area in organizations that is already changing due to advances in automation and AI. Unlike previous generations of rules-based technologies that improved speed or enhanced existing processes, AI brings the promise that it can actually become a collaborative team member. In a 2019 Forrester Consulting survey commissioned by my company, we found that while 88% of marketers say they are using AI, just 50% or less say “their current marketing stack supports their top objectives very well.” Why isn’t the transformative benefit of AI being realized? It turns out that of those using marketing AI, 74% are using their AI like old technology, to surface insights and recommendations that they consider and then manually take action on.
This “assistive AI” approach leaves most of the value of intelligent technology out of the equation. The beauty of an intelligent machine is its ability to be flexible and dynamic and to operate with success in mind-bogglingly complex environments. “Autonomous AI” that understands, predicts and can actually take action in real time is the answer to getting full value out of AI. But using it requires a significant shift in mindset from marketers. They need to move from operating machines to collaborating with them.
AI To Amplify Human Capacity
When used in a truly autonomous fashion either within or across channels, AI has the power to both drive campaign execution at scale and create deeper business value. However, there are two important things to remember: AI is not a cure-all, and AI will never come up with ideas.
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Consider findings recently debuted in the book Lemon. How the Advertising Brain Turned Sour, from Orlando Wood, chief innovation officer of System1. Despite the benefits that come from better adtech, including wider reach, more data and faster execution, Wood finds that “a golden age of advertising technology has not led to a golden age of advertising effectiveness.” Short-termism and media industry changes have shifted the marketer’s focus from inspiration and creativity to simply keeping up, but tools that can automate the more time-consuming, tactical parts of marketing can help.
AI is best thought of as a tool to amplify human capacity in tasks like number-crunching, identifying patterns in vast amounts of data and automating large, complicated and multivariate tasks. The optimal way to work with AI is to let it take over technological skills such as big data processing and prediction (in other words, supercharging marketing fundamentals) so that marketers have the bandwidth to devote to the social, emotional and cognitive skills needed to create inspiring, branded moments that resonate with customers. After all, only humans can bring the creativity and critical thinking needed to make meaning from data. I believe this will always remain the purview of the human workforce.
AI + Humans = The Second Golden Age of Marketing
Our survey with Forrester found that respondents who are already using AI solutions in an autonomous rather than assistive manner see a number of contributions to their marketing efforts beyond digital advertising. They tend to use data more effectively and engage their customers in a more personalized fashion — this alongside the improved returns on their digital advertising investments you might expect. Even marketers who have yet to embrace an autonomous AI-powered marketing solution understand the value: 95% of them find an autonomous marketing solution appealing for their organizations, according to our Forrester study.
Clearly, the future is coming. So, how do we prepare for it? Consider these steps to make sure your organization is equipped to embrace artificial intelligence:
1. Foster creativity in the workplace. There will be an increased emphasis on abilities like creativity, judgment and critical thinking that complement technology. Encouraging these skills now will set your teams up for success.
2. Define where a human and machine team can add the most value. By outlining where a machine’s role ends and a human’s role begins, you can identify organizational shifts that may need to occur or new roles that need to be defined.
3. Offer training on AI for the entire company. AI cannot be truly implemented well if your organization does not have a data-driven mindset.
4. Keep people in the loop. It’s critical for people to know what decisions the AI makes. This builds transparency and trust between technology and people while allowing those whose jobs are changing to play an important role in these human and machine teams.
5. Share AI’s insights to augment team relationships. Sharing learnings from AI with other team members can inform new strategies and plans, enhancing what people do well.
By playing to the unique strengths of humans and artificial intelligence, marketers can make better and quicker decisions, increasing productivity, revenue and outcomes.
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