10 USEFUL Artificial Intelligence & Machine Learning Slides

Evolution of Analytics

AISOMA - Evolution of Analytics
AISOMA – Evolution of Analytics

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
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)

10 Useful AI & ML Slides #MachineLearning #ML #ArtificialIntelligence #AI #DeepLearning #BigDataAnalytics #Chatbots #NLP #Industry40 #Slides #AIEthicsKLICK UM ZU TWEETEN

Artificial Intelligence Quote
Artificial Intelligence Quote

3. Machine Learning Workflow

AISOMA - Machine Learning Workflow
AISOMA – Machine Learning Workflow

Check out the Google Machine Learning Glossary

4. Deep Learning Workflow

AISOMA - Deep Learning Workflow
AISOMA – Deep Learning Workflow

Check out the Google Machine Learning Glossary

Artificial Intelligence is a Tsunami
Artificial Intelligence is a Tsunami

5. Deep Learning Continuous Integration and Delivery

AISOMA - Deep Learning CI and CD
AISOMA – Deep Learning CI and CD

More info: link

6. Anatomy of a Chatbot

AISOMA - Anatomy of a Chatbot
AISOMA – Anatomy of a Chatbot

More info: How Businesses can Benefit from Chatbots

7. Five ethical challenges of AI

10 Useful AI & ML Slides 1
AISOMA – 5 Ethical Challenges of AI

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
Artificial Intelligence quote

8. NLP / NLU Technology Stack

AISOMA - NLP 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)

9. Condition Monitoring / Predictive Maintenance Solution Architecture

AISOMA - Predictive Maintenance Solution Architecture
AISOMA – Predictive Maintenance Solution Architecture

More Info: Smart Predictive Maintenance: The Key to Industry 4.0

10. Artificial Intelligence in Marketing

AISOMA - AI in Marketing
AISOMA – AI in Marketing

5 failed tech predictions for the 2010s that didn’t work out

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 Zuckerberg testified 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.


A.I. & Retail: Carrefour propose,  en partenariat avec Google,  un sommelier virtuel pour aider ses clients  à choisir leurs vins (Source: Le Soir)

Google a aussi le nez fin

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…

In The Second Golden Age Of Marketing, Humans And Machines Can Thrive Together

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.

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.

Puces, 5G privée et vidéo publicitaire parmi les 10 tendances 2020 du marché des TMT selon Deloitte

Comme chaque année Deloitte décrypte les enjeux «Technologies, médias et télécommunications» dans ses prédictions. Cette 19ème édition met l’accent sur les technologies de plus en plus interconnectées et interdépendantes dans les smartphones, les ordinateurs, les téléviseurs, les centres de données d’entreprise et les logiciels.
Deloitte souligne par ailleurs que de nombreux services et produits auparavant très médiatisés deviendront enfin une réalité en 2020.
1- L’intelligence artificielle au cœur des smartphones
De nouvelles générations de puces d’accélération IA (edge AI chips) arrivent sur le marché. Ces architectures matérielles, optimisées pour le deep learning ou le traitement du langage naturel (NLP), permettent d’accélérer les tâches de machine learning directement sur les smartphones. Deloitte prévoit que plus de 750 millions de ces puces seront vendues en 2020 dans le monde.
2- Des réseaux 5G privés en test
Deloitte prévoit que plus de 100 entreprises à travers le monde testeront des réseaux 5G privés d’ici la fin 2020. Pour beaucoup des grandes entreprises mondiales, les réseaux 5G privés sont susceptible de devenir la norme, notamment dans les environnements industriels tels que les centres logistiques, les usines ou les ports.
3- Mon collègue, ce robot
Quasiment 1 million de robots seront vendus aux entreprises en 2020. Plus de la moitié d’entre eux seront des robots dédiés au service. Ces robots dépasseront les robots industriels en unités vendues en 2020 et en revenus en 2021.
4- Vidéo et pub
Les services de vidéos financés par la publicité généreront 32 milliards de dollars de revenus en 2020. Presque la moitié de ce volume d’affaires – 15,5 milliards de dollars – sera généré en Asie où ce modèle est prédominant, contrairement aux Etats-Unis où la SVOD sans publicité prévaut.
5- L’antenne TV  terrestre soutient le média TV
La consommation de programmes TV par le biais d’une antenne (TV analogique ou TNT) continue de se développer. En 2020, au moins 1,6 milliard de personnes à travers le monde, soit 450 millions de foyers, regarderont la TV de cette manière. Une tendance qui permet à l’industrie télévisuelle de continuer à croître malgré l’érosion du temps passé devant le petit écran.
6- L’essor des satellites en orbite basse
D’ici la fin 2020, plus de 700 satellites en orbite basse offriront un accès internet aux habitants de la planète. Ils étaient à peine 200 fin 2019. Selon Deloitte, ces nouvelles «mega-constellations» vont envoyer dans l’espace plus de 16 000 satellites supplémentaires dans les années qui viennent.
7- Livres audio et podcasts
En 2020, le marché des livres audio va croître de 25% pour atteindre 5 milliards de dollars. Quant au marché des podcasts, sa progression sera de 30% par rapport à 2019 et dépassera le seuil du milliard de dollars (1,1 milliard de dollars précisément).
8 Un smartphone encore plus smart
L’écosystème des smartphones, dans lequel Deloitte inclut le matériel, les contenus et les services liés à ce terminal, va générer 459 milliards de dollars de revenus l’an prochain. Il continuera d’augmenter chaque année de 5 à 10% jusqu’en 2023.
9 L’explosion des marchés des réseaux de diffusion de contenu
Le marché des CDN (Content Delivery Network) atteindra les 14 milliards de dollars en 2020, en hausse de 25% par rapport à 2019 (11 milliards de dollars). Ce marché devrait doubler d’ici 2025, pour atteindre un volume d’affaires de 30 milliards de dollars en 2025, suivant un taux de croissance annuel composé de plus de 16%.
10- Le vélotaf en vogue
Des dizaines de milliards de trajets supplémentaires en vélo vont être effectués d’ici 2022. Le nombre de personnes utilisant leur vélo pour aller travailler va doubler dans les villes où cet usage n’est pas encore répandu. Selon Deloitte, plus de 130 millions de vélos électriques seront vendus entre 2020 et 2023.

Marketing Technology Trends for 2019 and 2020: more than 75% of the customers expect that companies understand their needs and expectations

Author: Avi Ben Ezra is the Chief Technology Officer (CTO) and Cofounder of SnatchBot and SnatchApp (Snatch Group Limited)

Source: https://www.newstrail.com/marketing-technology-trends-for-2019-and-2020/


Marketers, today have a variety of tools available to them to offer consumers and business buyers the convenience, relevance and responsive engagement expected. Customers are now better connected than ever before, they have presence and interact across a broad range of media. The fast pace with which marketing continues to move forward means that it is extremely easy to be left behind and the fast developments in marketing technologies are not something to be ignored. They not only make predictions easier, but also take a load of work off the shoulders of the employees in the marketing department but also increase the way that the rest of the company interacts with its customers, giving them the personalization and interaction that they demand. A good marketing program necessitates that marketers make use of these modern technology trends, since they understand the needs and behaviors of their consumers better than everyone else, presenting initiatives that will continue transforming their company campaigns to meet the challenges ahead.

With the second quarter of 2019 already gone, what are those technology trends that will continue to shape the way marketers plan ahead?

Taking the statistics from previous years and comparing then with the present, there is a significant rise in marketing tech users and companies are seeing noteworthy results in their competitive advantage.

It is estimated that the sharing of marketing metrics with sales teams has grown at a rate of 21% in the last year and a half and similarly, so have the number of data sources used by marketers. The adoption of AI is increasing at an even faster rate and marketers are making more use of coordinated channels too.

What is driving these increases?

The driving force behind the increases is the connected customer, who expects convenience, relevance and responsive engagement. Consumers judge a company from their overall experience and never separate interactions with various departments. Therefore, it is important that they see the company that they buy from as one with the company that they interact with and that they get the same level of service across all departments.

According to consumer surveys, today’s consumers don’t only have high standards for product quality, but also expect these to be matched in other interactions with other services that they might need. Competition is stiff and consumers have all the information about competitors available in seconds through their Smartphones and laptops.

Gone are the days when each department worried about its own performance. Today, sales, customer service, commerce and marketing are responsible for ensuring the entire consumer experience – no department can afford to act independently from the other. The initiatives for these efforts always fall onto the marketing department since it completely understands customer needs and behavior.

How can marketers lead their companies to success?

These three essential technology trends will enable marketers to continue to direct businesses in a successful direction.

  1. Personalization is the top priority

The majority of customers, more than 75%, expect that companies understand their needs and expectations. At the same time, over 50% expect that any offers they receive are personalized. Therefore, personalization is a crucial element and comes with important benefits which include: brand building, lead generation, customer acquisition, up-selling; customer retention and customer advocacy.

Artificial intelligence (AI) is one of the most important marketing tech tools for personalization because it aids marketers to unlock the data needed.

The capabilities of AI are expand continuously and so do the ways that marketers use it: predictive journeys, real-time offers, improved customer segmentation,  personalized channel experience, automated social and messenger app interactions, dynamic landing pages and websites, media buying, and offline/online data experience facilitation and programmatic advertising.

Personalization is important to customers, but so is transparency about how that data is used. Regulators also worry about transparency and recent data breaches have also shaken consumer confidence.

High percentages (over 75%) of customers do trust companies with personal information if it is to be used to fully personalize their experience, but they demand clarity on how the information will be used.

In the past two years, marketers are more mindful about how privacy and personalization can be balanced in order to fully satisfy their customer needs.

  1. Making sense of customer data

Data sources for marketers abound as they go about tallying email open rates, ad click rates and more. This information allows them to engage the right individual with the right information and also at the right time. In 2017, the median data sources available to marketers were 10 and the number has shot up by 15% already in a period of just 2 years.

More data does not necessarily mean a more unified view, as many marketers struggle to make sense of all the data that becomes available. A mix of various solutions is often the preferred set up for some marketers, where they unite data from marketing databases to email service providers. Another one of the fast growing areas in marketing tech is data management platforms (DMP) which are offering a wide range of solutions for this problem and for many others too.

Originally, DMP was mostly used for monitoring ad performance and optimizing media campaigns. Organizations have evolved its uses and now often include the management of customer identity and other solutions. DMP use is on the rise and it will continue to offer marketers a unified solution to their customer identity challenges and vital opportunities in marketing management.

  1. Cross-channel marketing

The top challenge and priority for marketers is to have the ability to engage customers in cross-channel, real-time conversations. This is where proper use of RPA and omni-channel chatbot solutions are valuable.

Even though cross-channel marketing is not a new concept, it is not always easy to achieve since most customers now use an average of 10 channels in their communications with companies. Standard expectations are to have two-way communication with customers. Most messages across channels are duplicates, and there is no coordination between them. The ideal communication, something which most companies are aiming to achieve, is to engage customers dynamically across channels through messages that evolve across each communication channel.

The channels of communication are mostly: website, mobile app, social advertising, video advertising, social publishing, Email, mobile messaging, banner ads, paid search (SEM) and voice activated personal assistants.

Marketers need to start meeting the expectations that customers have for cross-channel engagement. Marketing technology allows for such collaboration between marketing and the other teams within the business, allowing it to be more competitive.

The integration of new technologies will be the way forward for businesses in order for them to be able to better enhance their customer experience. Of the marketing technologies that are expected to have the biggest impact on interactions, data collection, and personalization are the technologies of artificial intelligence and augmented reality.


Marketers need to take time to start understanding how the digital landscape and the technology it offers can improve the performance of their campaigns. Perhaps, starting with the implementation of one trend to may be enough, but incorporating them cohesively can lead to even greater results. Suggested reading: Avi Ben Ezra on Chatbot deployment in Europe. 

L’intelligence artificielle est pour Porsche la technologie la plus importante pour l’avenir … (une brève histoire de l’A.I.)

Les progrès technologiques rapides dans le domaine font de l’intelligence artificielle (IA) l’une des technologies clés du 21e siècle – grâce au Big Data et à l’augmentation exponentielle des capacités informatiques. Le constructeur automobile Porsche mise beaucoup dessus. Mais quel est son fonctionnement?

source: https://www.4legend.com/2019/lintelligence-artificielle-est-pour-porsche-la-technologie-la-plus-importante-pour-lavenir

Il y a 50 ans, L’IA (AI en anglais) était devenue pour la première fois un concept grand public – sous la forme d’un film hollywoodien. En 1968, le réalisateur Stanley Kubrick demanda au supercalculateur HAL 9000 de prendre le contrôle du vaisseau spatial dans son épopée de science-fiction «2001 : l’odyssée de l’espace». Une machine plus intelligente que l’homme?

En tant que discipline universitaire, L’IA avait 12 ans lorsque le film est sorti. En juillet 1956, elle est arrivée au célèbre Dartmouth College, dans le New Hampshire (États-Unis). C’est alors qu’un groupe de mathématiciens et d’ingénieurs électriciens ambitieux s’est réuni au Dartmouth Summer Research Project. Artificial Intelligence est un projet initié par John McCarthy, qui a inventé le LISP – le deuxième langage de programmation le plus ancien au monde.

Naissance de « l’intelligence artificielle »
Après quelques semaines laborieuses cet été-là, les dix penseurs invités avaient produit une plume d’écriture dense et de nombreuses idées. Machines parlantes; des réseaux basés sur le cerveau humain; ordinateurs auto-optimisants; et même la créativité de la machine semblait être à la portée de cette génération fondatrice euphorique. Cependant, leur développement le plus important a été l’expression «intelligence artificielle», qui a donné naissance à une nouvelle discipline qui fascinerait les gens du monde entier à partir de ce moment – et qui a en fait été adopté plus rapidement que prévu.

La même année, Arthur Lee Samuel – l’un des participants à la conférence et un informaticien du Massachusetts Institute of Technology (MIT) – a enseigné à un ordinateur IBM 701 comment jouer aux contrôleurs de jeux de société. Son programme utilisait une méthode permettant à la machine de tirer des leçons de sa propre expérience, en particulier dans les versions ultérieures. En 1961, il a joué contre le champion d’État du Connecticut – et a gagné. Cette approche représentait l’idée de base de l’IA en action : l’apprentissage de logiciels sur la base de grandes quantités de données.

Chant d’ordinateur
Cette année-là également, un ordinateur de type 704 a appris la chanson «Daisy Bell» aux laboratoires Bell et l’a reproduite à l’aide d’une synthèse vocale. Cela a évidemment attiré Stanley Kubrick, car il avait fait chanter la même chanson dans le film du superordinateur HAL 9000. Pour les masses à l’époque, tout cela était de la pure science-fiction; mais aujourd’hui, personne ne tombe de sa chaise avec surprise si leur ordinateur joue de la musique. C’est une autre des capacités de HAL 9000 qui reste encore hors de portée: une IA «forte» ou «générale», c’est-à-dire une IA qui imite complètement ou qui pourrait même remplacer les humains, reste un rêve utopique.

Le test de Turing est appliqué pour déterminer si un développement d’intelligence artificielle est comparable à celui d’un humain. Bien qu’aucun système technique ne réussisse ce test dans un avenir prévisible, les machines peuvent déjà faire mieux que les humains. Par exemple, ils sont extrêmement utiles pour analyser de grandes quantités de texte ou de données et constituent le socle des moteurs de recherche Internet. Intégrées dans d’innombrables applications pour smartphone, nous emportons cette IA «faible» dans nos poches où que nous soyons – et en tant qu’utilisateur, nous en sommes presque à peine conscients. Mais tous ceux qui parlent à Alexa ou à Siri voient également leurs phrases analysées à l’aide d’algorithmes d’intelligence artificielle; John McCarthy a fait un commentaire sans prétention sur le sort des applications d’intelligence artificielle: «Dès que cela fonctionne, personne ne l’appelle plus« intelligence artificielle ».

Deep Blue bat le champion du monde d’échecs Garry Kasparov
L’étonnement est de mise à chaque fois qu’Amnesty International franchissait une nouvelle étape, par exemple en 1997 lorsque Deep Blue battait le champion du monde d’échecs sur ordinateur, Garry Kasparov. Les jeux sont toujours un banc d’essai populaire pour les scientifiques de l’IA, et ils offrent également de bonnes possibilités de publicité.

Jeopardy, par exemple, est un jeu télévisé qui implique que les candidats doivent identifier la bonne question à laquelle un terme donné est la bonne réponse. Les tâches définies étaient généralement formulées de manière à être délibérément ambiguë et à exiger la mise en relation de plusieurs faits pour trouver la bonne réponse – rendant le défi beaucoup plus difficile. Cependant, le système IBM «Watson» a réussi à battre les deux détenteurs de records humains en 2011, après avoir été alimenté avec 100 gigaoctets de texte. Plutôt que de s’appuyer sur un algorithme individuel, Watson en utilisait simultanément des centaines pour trouver une réponse potentiellement correcte via un chemin. Plus nombreux sont les algorithmes qui parviennent indépendamment à la même réponse, plus grande est la probabilité que Watson parvienne à la bonne conclusion.

DeepMind bat le champion du monde « Go » Lee Sedol
DeepMind, une start-up londonienne fondée en 2010 et intégrée au groupe Google en 2014, a ensuite suscité un vif enthousiasme. Elle a développé une application d’intelligence artificielle optimisée lors de l’apprentissage de jeux. AlphaGo s’est fixé pour objectif de battre un champion du monde «Go» humain – ce qui était considéré comme une tâche presque insurmontable étant donné l’extrême complexité de ce jeu de stratégie. AlphaGo a atteint son objectif pour la première fois en 2016, en battant le champion du monde en titre Lee Sedol de Corée du Sud : un jalon attendu depuis longtemps. Actuellement, le programme AlphaZero ne se défait que de lui-même, car il renonce aux exemples de jeux humains et n’apprend qu’en jouant seul : les joueurs humains n’ont plus aucune chance de gagner contre AlphaZero.

Cet exploit est rendu possible par les réseaux de neurones artificiels. Les neurones sont des cellules nerveuses qui forment un réseau auquel une tâche individuelle est attribuée, telle que la vision. Un nombre apparemment infini de neurones sont connectés de manière dynamique dans le système nerveux humain. Le cerveau humain apprend en ajustant la densité de ces réseaux sur une base continue. Les chemins fréquemment utilisés deviennent plus forts, tandis que les connexions négligées dépérissent.

Réseau neuronal artificiel
Un réseau de neurones artificiels tente de reproduire cette structure. Les neurones artificiels en réseau prennent en compte les valeurs d’entrée et introduisent ces informations dans des neurones créés dans des couches de niveau inférieur. À la fin de la chaîne, une couche de neurones de sortie fournit une valeur de résultat. La pondération variable des connexions individuelles confère au réseau une propriété particulièrement remarquable : la capacité d’apprentissage. Aujourd’hui, les réseaux sont de plus en plus basés sur ces niveaux; ils sont plus complexes et plus entrelacés, c’est-à-dire plus profonds, grâce à une capacité informatique accrue. Certains réseaux de neurones profonds sont constitués de plus de 100 couches de programme connectées en série.

Cependant, l’IA doit être formée – dans le cadre d’un processus également appelé apprentissage en profondeur. Dans ce processus, les systèmes reçoivent des informations correctives provenant d’une source externe, par exemple un humain ou un autre logiciel. Le système tire ses conclusions des réactions qu’il reçoit – et il apprend.

Tests pratiques prometteurs chez Porsche
Le DSI de Porsche, Mattias Ulbrich, estime que l’intelligence artificielle est la technologie la plus importante pour l’avenir et qu’elle nous aidera à consacrer tout notre temps à ce qui compte vraiment. « L’IA participera à la création de valeur. De la même manière que les robots nous soulagent déjà physiquement aujourd’hui, l’IA nous aidera à réfléchir et à prendre des décisions lors de travaux de routine », explique-t-il. Les départements de développement ont beaucoup de travail à faire avant que nous atteignions ce point. Une considération clé dans ce travail concerne les aspects de la sécurité et de la vie privée.

Tobias Große-Puppendahl et Jan Feiling, du département principal du développement de l’électronique et de l’électronique, ont abordé le sujet chez Porsche. Les développements tels que la personnalisation, l’intelligence des essaims et la protection de la sphère privée nécessitent tous une IA afin de préserver la confidentialité absolue lors de la collecte et de l’échange de données. L’équipe a pour objectif de minimiser l’échange de données en utilisant un «apprentissage fédéré» dans lequel un système d’intelligence artificielle local situé dans la voiture tire les enseignements du comportement de l’utilisateur. Par exemple, si un conducteur dit «J’ai froid», l’IA devrait augmenter le chauffage. Il transmet son succès d’apprentissage – ou pour le dire autrement, son expérience – au cloud et aux instruments globaux d’IA installés dans ce pays, tandis que des données spécifiques telles que des protocoles de langage peuvent rester dans la voiture. En fin de compte, l’intention derrière les données est la clé : chaque utilisateur exprime un souhait à sa manière, mais attend le même résultat. Pensez à rencontrer une personne dont nous ne comprenons pas la langue, mais elle est en mesure de préciser si elle a froid.

Science fiction pure
Bien sûr, HAL 9000 de « 2001 : l’Odyssée de l’espace » de Stanley Kubrick est également capable de le faire. Mais une rébellion d’IA contre les humains est une pure science-fiction – du moins pour le moment – et la téléportation telle que vue dans Star Trek restera probablement pour toujours un rêve utopique. Après tout, une bonne science-fiction ne reflète pas exclusivement une technologie de pointe peu connue du grand public – telle que l’ordinateur chantant – mais explore également les domaines de l’incroyable fantaisie. Le professeur Sebastian Rudolph, spécialiste de l’IA basé à Dresde, estime que les futurs scénarios de rébellion artificielle sont extrêmement farfelus compte tenu de l’état actuel de la technologie. Il dit que, comme c’est le cas pour toutes les technologies, l’IA pourrait être mal utilisée – et en fait que des erreurs pourraient être commises lors de sa mise en œuvre.

Nous ne devrions donc peut-être pas avoir plus ni moins peur de ce type de développement que du progrès technique en général. Et vu de cette façon, il est logique que nous participions tous à la conception de ce progrès technique. C’est ce que Tobias Große-Puppendahl et Jan Feiling ont intériorisé chez Porsche – et en fait dans le meilleur de la tradition de la société, à la suite de Ferry Porsche lui-même : « Nous ne pouvions pas trouver d’IA qui nous séduisait. Alors nous l’avons construit nous-mêmes. »

The Current Applications Of Artificial Intelligence In Mobile Advertising (source: Forbes)

The concept of self-programming computers was closer to science fiction than reality just ten years ago. Today, we feel comfortable conversing with smart personal assistant like Siri and keep wondering just how Spotify guessed what we like.

It’s not just the mobile apps that are becoming more “intelligent”. Advertising encouraging us to interact and install those apps has made its way onto a way new quality level as well. Thanks to advances in machine learning (ML), the baseline technology for AI, mobile advertising industry is now undergoing significant transformation.

source: https://www.forbes.com/sites/andrewarnold/2018/12/24/the-current-applications-of-artificial-intelligence-in-mobile-advertising/#2c8fa7f91821

AI can reduce mobile advertising fraud

In 2018, mobile ad fraud rates have doubled compared to the previous year. To tap into the expanding marketer’s ad budgets, hackers have created a host of new tricks to their playbook. According to Adjust data, the following mobile ad threats have prevailed:

SDK spoofing accounts represented 37% of ad fraud. In SDK Spoofing malicious code is injected in one app (the attacked) that simulates ad clicks, installs and other fake engagement and sends faulty signals to an attribution provider on behalf of the “victim” app. Such attacks can make a significant dent in an advertiser’s budget by forcing them to pay for installs that never actually took place.

Click injections accounted for 27% of attacks. Cybercriminals trigger clicks before the app installation is complete and receive credit for those installs as a result. Again, these can drain your ad budgets and dilute your ROI numbers.


Faked installs and click spam accounted for 20% and 16% of fraudulent activities respectively. E-commerce apps have been in the fraud limelight this year, with nearly two-fifths of all app installs being marked as “fake” or “spam”, followed closely by games and travel apps. Forrester further reports that 69% of marketers whose monthly digital advertising budgets run above $1 million admit that at least 20% of those budgets are drained by fraud on the mobile web.

If the issue is so big, why no one’s tackling it? Well, detecting ad fraud is a complex process that requires 24/7 monitoring and analysis of incoming data. And that’s where AI comes to the fore. Intelligent algorithms can operationalize large volumes of data at a pace far more accurate than any human analyst, spot abnormalities and trigger alerts for further investigation. What’s more promising is that with advances in deep learning, the new-gen AI-powered fraud systems will also become capable to self-tune their performance over time, learning to predict, detect and mitigate emerging threats.

AI brings increased efficiency and higher ROI for real-time ad bidding

One of the biggest selling points of “AI revolution” across multiple industries is the promise to automate and eliminate low-value business processes. Mobile advertising is no exception. Juniper Research predicts that by 2021, machine learning algorithms that increase efficiency across real-time bidding networks will drive an additional $42 billion in annual spend.

Again, thanks to robust analytical capabilities ML-algorithms can create the perfect recipe for your ad, displaying it at the right time to the right people. Google has already been experimenting with various optimizations for mobile search ads. The results so far are rather promising. Macy’s, for instance, has been leveraging inventory ads and displaying them to customers’ who recently checked-up on their products and are now in close geo-proximity to the store holding the goods they looked up a few hours ago.

AdTiming has been helping marketers refine their approach to in-app advertising. By leveraging and crunching data from over 1000 marketers, the startup has developed their recipe for best ad placements. “Prescriptive analytics will tell our users when is the best time to run their ads; what messaging to use and how frequently the ad needs to be displayed in order to meet their ROI while maintaining the set budget,” said Leo Yang, CEO of AdTiming.

Just how competitive AI-powered real-time ad bidding can be? A recent experiment conducted by a group of scientists on Taobao – China’s largest e-commerce platform – proves that algorithms are performing way better than humans.

For comparison:

  • Manual bidding campaigns brought in 100% ROI with 99.52% of budget spent.
  • Algorithmic bidding generated 340% ROI with 99.51% of budget spent.

It’s clear who’s the winner here.

AI enables advanced customer segmentation and ad targeting

Algorithms are better suited for detecting patterns than a human eye, especially when sent to deal with large volumes of data. They can effectively group and cluster that data to create rich user profiles for individual customers – based on their past interactions with your brand, their demographic data and online browsing behaviors.

This means that you are no longer targeting a broad demographic of “women (aged 25-35), based in the US”. You become capable to pursue more niche audiences, exhibiting rather specific behaviors e.g. regularly engaging with hair care products in the luxury segment on social media. This insight can be further applied by an AI system when entering an RTB auction to predict when your ad should be displayed in front of the consumer (matching your profile) and when it’s worth a pass.

The best part is that AI-powered advertising is no longer cost-prohibitive for smaller companies. With new solutions entering the market, it would be interesting to observe how the face of mobile advertising will change in 2019 and onward.

IBM Watson Marketing Releases 2019 Marketing Trends Report Focused on Emerging Trends Redefining the Profession in the Shift to AI

IBM Watson Marketing Releases 2019 Marketing Trends Report Focused on Emerging Trends Redefining the Profession in the Shift to AI

Top Drivers Include “the Emotion Economy,” Tech-First Marketers, and AI-Powered Personalization, Supported by New Roles in Data and Agile Marketing

With artificial intelligence (AI) continuing to gain usage among marketing professionals, IBM announced a new report from IBM Watson Marketing identifying a new breed of marketers coming to the forefront. Driven by a growing need to meaningfully structure data to enable actionable, real-time decision making IBM’s 2019 Marketing Trends Report provides practical insights for businesses to stay ahead of the forces driving these changes.

“It’s not often an entire profession experiences a sea change the likes of which has profoundly impacted all facets of marketing, but we are in that moment,” said Sylvia Vaquer, Co-Founder & Chief Creative Officer, SocioFabrica. “This transformation fueled by AI-powered inferential connections provides a rare opportunity to rearchitect the whole ‘marketing house,’ but it’s critical to have practical knowledge to capitalize on it.”

Also Read: HG Data Audience Extends Technographics to Facebook, Adobe, Twitter, and Salesforce Digital Marketing Platfor

The report’s findings provide deep insights into how CMOs and digital agencies are reimagining the marketing function with the following overview giving a blueprint for what it will look like in 2019 and beyond:

  • In The Emotion Economy, Purpose Creates Brand Loyalty – More than ever, consumers are more likely to engage with brands that are authentic, meaning the brand holds strong convictions and delivers on them, versus experience alone.
  • Marketer 4.0: Emergence of the Tech-Savvy “Martecheter” – Until now the greatest advantages for marketers has been in the order of budget, tools and talent. That model is now inverted, driven by the rapid growth of new skills and customer expectations.
  • AI and Machine Learning Make Hyper-Personalization a Reality – The proliferation of data and compartmentalized marketing stacks has squarely put AI and machine learning-based marketing tools at the center of deep personalization. This will change how marketers make decisions and deploy campaigns as AI analyzes and delivers personalized content with massive scale.
  • Director of Marketing Data Becomes the Hottest New Role – Growth of “marketing data” roles will continue as they drive human and technology connections across their organization. This will enable artificial intelligence and machine learning-based marketing tools to analyze data and customer behavior, make recommendations and predictions, and become smarter based on the data fed into them.

Also Read: Okta Names Shellye Archambeau to Board of Directors

  • Agile Marketing Adoption Accelerates, Driving Marketing Outcomes and Culture – Organizations driven by culture change and agile mindsets will widen their lead having a first-mover advantage, especially where AI-powered marketing technology enables the right set of tools to align and measure the proper objectives and metrics.
  • GDPR Actually Helps Marketers Improve Data Hygiene and Customer Trust – Marketers will increasingly focus on improving data hygiene processes, leading to better targeting and higher quality interactions. With similar regulations already in certain parts of the US and the potential for US-wide legislation, marketers will proactively improve privacy, security, and data management as a catalyst for new business models.
  • Digital Marketing Agencies Transform into “Consulgencies” – As the rankings of AdAge’s 10 largest agencies was cracked – for the first time ever – by The Big Four consultancies in 2017, it serves as a bellwether for the industry including smaller and mid-sized agencies: “Consultancy” and “agency” capabilities will converge, driven by a need to build out deep expertise in AI, data integration, customer experience analytics, mobile apps, and custom solution development.
  • MarTech + AdTech = The Holy Grail of Marketing – Although extensively debated, 2019 will at last be the year marketers lean more into the benefits of programmatic ad spending. By achieving better data connectivity between their martech and adtech stacks they will have real-time understanding of customers and ad spend optimization using AI.
  • Customer Centricity Breaks Marketing Silos and Delivers Happiness – Marketing transformations will increasingly focus on creating differentiated customer experiences. This will be supported by more experimentation using contextualized understanding of aggregated customer data across other areas of the organization such as commerce and digital teams.

“As marketers ourselves, we felt it was critical to dissect the industry dynamics that are reshaping the profession to help our peers set a strong vision for their organizations.” said Michael Trapani, Marketing Program Director for IBM Watson marketing. “By understanding the transformative trends – from team structure, to the advent of AI-powered marketing, to heightened expectations for the discipline – the findings in the 2019 Marketing Trends Report will be indispensable for marketers.”