Data Science / AI
Data Science leverages data and uses the most advanced technologies and algorithms to create knowledge and drive predictions. By developing analytical models, it makes possible to explain, predict and automate decisions.
Data Science is the root for Artificial Intelligence and its uses are exploding. There are many disciplines that are related to Data Science: Artificial Intelligence, which includes Machine Learning and Deep Learning, Statistics, Data Preparation, Data Visualisation (Dataviz).
Integrating AI and Data Science
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Integrating AI and Data Science
Generative AI: 4 steps to deploy it in your business
ChatGPT and similar tools have acted as catalysts, bringing generative AI into the mainstream for the general public. But make no mistake, the greatest value of generative AI will be found within companies, by adapting these tools to their specific needs and knowledge base. To achieve this, a structured and iterative deployment is essential, one…
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Integrating AI and Data Science
Adopting generative AI in the enterprise: what methodology should you implement?
74% of business leaders believe the benefits of generative AI will outweigh the concerns, according to the Capgemini Research Institute report Harnessing the value of generative AI: Top use cases across industries. Yet only 19% of companies have launched concrete initiatives. So where to begin? This article outlines the key steps. What are the prerequisites…
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Integrating AI and Data Science
The different types of generative AI use cases in business
How can generative AI be used in business? What new opportunities does the technology unlock, and what are its limitations? Like any emerging solution, generative AI raises questions and sometimes concerns because it introduces entirely new ways of working. The following examples around text generation illustrate this shift. While many applications of generative AI are…
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Integrating AI and Data Science
How can we deploy trustworthy AI?
68 percent of employees use ChatGPT at work without their employer’s knowledge. That means more than two out of three companies are unintentionally sharing personal or confidential data with a public, unsecured application. This alarming reality raises important questions about the ethical and legal implications of generative AI, and more broadly, about what it means…
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Integrating AI and Data Science
Green AI: Responsible artificial intelligence is also frugal
When it comes to Artificial Intelligence, it’s not only about improving performance at any costs. Its benefits along its adoption requires AI to be responsible by also including an environmental side. Taking environmental issues into account is no longer an option. The IPCC’s April 2022 report is clear. It is also, pun unintended, one of…
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Integrating AI and Data Science
AI Industrialization: the key steps to a MLOps approach
The industrialization of artificial intelligence – one of the 7 hot data topics for 2022 requires the implementation of MLOps. This approach includes some necessary steps, including a common platform and a feature store. To learn more about this approach, we offer you a how-to-guide for an iterative, but unavoidable transformation. After years, which were…
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Integrating AI and Data Science
Data Science applied to Vertical Farming: the future of sustainable farming
By 2050, there will be over 6.5 billion people living in urban spaces according to a United Nations report, making it a real future challenge to feed them all. Vertical farming is becoming a critical component of agriculture’s future. This concept aims to optimize plant growth and soilless farming techniques using as little space and…
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Integrating AI and Data Science
Data Ethics/AI Ethics: the 2 faces of a responsible future
Artificial Intelligence is at the heart of all attentions and concerns right now. Did you know that the real difficulty with Artificial Intelligence is not the algorithms, nor the design of the models but it is above all the Data! And yet, Data is increasingly distrusted today. How to solve that and produce trusted &…
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Integrating AI and Data Science
Artificial Intelligence: Stay in control of your future!
If there is one topic that really ignites passion and fuels all ideas and discussions in the world of new technologies, it’s Artificial Intelligence. What are the opportunities for enterprises? How to launch AI projects? What are the best practices, benefits, and risks? You will find all the answers in this white paper, available and…
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Integrating AI and Data Science
Statistics versus Machine Learning: should they really be opposed?
This “seemingly” old debate deserves to be revisited with fresh perspective. Data Science (such as Big Data) is a constantly evolving field with nowadays proven applications namely in the fields of customer knowledge and marketing… Statistics and machine learning in the era of Data Science and customer knowledge Even though the field of application is fairly recent, the basic methods used in Data…
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Integrating AI and Data Science
The 5 key Data Science practices
In the wake of Big Data, many companies embarked on the Data Science journey, the field having established itself as the inescapable route towards Big Data transformation into knowledge and actions. Discover in this blog article the 5 key practices to observe in order to ensure project success. 1. Methodology Data Science methodology is essentially agile and iterative. It derives from inductive reasoning, which…
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Integrating AI and Data Science
The key to Data Science success is the CRISP methodology
The CRISP methodology (originally known as CRISP-DM), first developed by IBM in the 60s for data mining projects, remains, today, the only truly efficient process used for Data Science projects… CRISP methodology: User guide The CRISP methodology includes 6 steps that range from business understanding to deployment and implementation. 1. Business understanding The first step involves acquiring a good understanding of the business elements and issues that Data Science aims to improve or solve. 2. Data…
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Integrating AI and Data Science
Data Science and AI: how to properly scope your business projects?
An increasing number of companies are opting for data-driven strategies and embarking on marathon Data Science and Artificial Intelligence projects, in the hope of sharing the benefits of new technologies and data. What is the best way to ensure the success of Data Science or Artificial Intelligence projects? Which players will help effect change and…
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Integrating AI and Data Science
How is the Port of Antwerp optimising logistics with data science?
Looking for fast, intelligent exploitation of its mass of data, the Port of Antwerp turned to Orange Business to optimise and secure the safety and efficiency of its maritime transport. A report.
Understanding AI and Data Science
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Understanding AI and Data Science
RAG: enhancing generative AI with enterprise data
RAG has become a key concept for anyone looking to create real value with generative AI based on their organisation’s data. Let’s take a closer look at what it means and explore the best enterprise approaches to make a real impact, interview-style 😉 What does RAG mean, and when is it used? Mick Levy: RAG…
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Understanding AI and Data Science
Generative AI: the best use cases for business
Generative AI made a dramatic entrance into the mainstream with the rise of ChatGPT. But beyond the buzz and media fascination, it is now time to seriously consider how these tools can be integrated into businesses. As with any major innovation, early adopters will gain a competitive edge. And here, the real game-changer lies in…
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Data and AI news
Generative AI is not a sprint… but a Marathon
Discover in this webinar how to not only sprint off the starting blocks but also conquer the long-distance race to AI success! Ready, Set, Innovate! Discover how Generative AI is revolutionizing businesses by shifting from quick wins to long-term transformative strategies. Hosted by Pierrick Reglioni (Data & AI Expert), with expert insights from Jérémy El Aissaoui…
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Understanding AI and Data Science
What are the core technologies behind generative AI?
As mentioned in our article “The development of generative AI: what impact on business?”, generative AI is not a recent concept. The neural networks it relies on have been known for decades. So why did it take until 2022 for this technology to gain widespread attention? The answer lies in a series of technological breakthroughs,…
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Understanding AI and Data Science
From impacts to challenges: generative AI in business
ChatGPT, DALL·E, Midjourney… According to recent global surveys, over half of business leaders see generative AI as a new industrial revolution. Nearly half believe it will profoundly transform the way we work. But what exactly are the challenges and impacts of this technology on business? This article takes a closer look. Time and productivity gains…
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Understanding AI and Data Science
The direct impact of Artificial Intelligence on Human Resources what ChatGPT and we have to say
The direct impact of AI on employees – and therefore on Human Resources (HR) departments – is, in our view, an urgent matter to address if it has not already been considered. It also provides an opportunity to explore how data science can support HR directors in their strategic missions. In this article, we will…
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Understanding AI and Data Science
ChatGPT, Midjourney, LLM… But what exactly is generative AI?
With ChatGPT leading the way, alongside MidJourney and DALL·E, generative artificial intelligence has entered the mainstream—though not without creating some confusion. Generative AI technologies cover a wide range of tools and applications that businesses need to understand and harness. Here’s a closer look to clarify and demystify the subject. At the end of 2022, OpenAI…
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Understanding AI and Data Science
Generative AI: who are the key players in the market?
The generative AI market is still very young. As a result, the number of players remains relatively limited for now. Two main categories stand out: the GAFAMs on one side, and start-ups on the other. Why are there so few mature players in this field? Because in artificial intelligence, the real battleground lies in access…
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Understanding AI and Data Science
The development of generative AI: what impact on businesses?
In January 2023, just two months after its launch, the ChatGPT website was already receiving an average of 13 million unique visitors per day, with daily traffic growing by 3.4%. By April, the site was welcoming one billion monthly visits, including nearly 100 million active users. Why do generative AI and Large Language Models (LLMs)…
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Data and AI news
#Data / #AI: our experts analyze the trends for 2022
In 2022, data and AI are more than ever a key priority for companies. For the sixth consecutive year, Orange Business is presenting the key trends in Data and AI topics for businesses. Discover more in this webinar available in replay (and in English for the first time!) Our experts will comment and give you…
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Data and AI news
Artificial Intelligence to better protect us in times of pandemics
Artificial Intelligence & Machine Learning technology are playing a key role in better understanding pandemics. More specifically, AI can support decision makers in taking the right actions to handle a pandemic. We discussed with Dr. Pieter Libin, Professor at the AI lab of the Vrije Universiteit Brussel, how Artificial Intelligence, and more precisely, Reinforcement Learning,…
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Understanding AI and Data Science
Can a whole Data Science project be done using R or Python?
For several years now, many Data Scientists have found themselves turning to “language” command line tools, such as R and Python, to deal with Big Data. But can you really undertake a whole Data Science project solely armed with these two technologies? The evolution of Data Science tools Looking back on the evolution of what is known today as Data science, (which,…
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Understanding AI and Data Science
Does Auto-Machine Learning (AutoML) really exists?
Automated machine learning (AutoML) has existed since 1990, it was considered as a silent revolution in the Artificial Intelligence (AI) field. When we analyze the term AutoML, we see that it refers to two words, Automated and Machine Learning. Machine Learning with its different types of learning Supervised (Labeled data) Unsupervised (Unlabeled data) Semi-supervised (A…
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Understanding AI and Data Science
Data Science: the 4 obstacles to overcome to ensure a successful project
The last five years we have seen the number of Data Science projects carried out by Orange Business in various sectors, such as the oil industry, telephony, retail and services, rise significantly. However, some difficulties must be overcome in order to efficiently implement these types of projects. Explanation. First of all, let us not forget…
