The artificial intelligence (AI) road from unlimited (yet largely nonspecific) potential to concrete, specific business benefits, is like taking a long road trip with kids -- palpable excitement alternated with restless tension and cries of "Are we there yet?" So, are we "there" yet? Well, no, but we are certainly closer than we have been, and by further examining the data that underlies these systems, we can progress closer to "there" by recognizing measurable ROI from the ability to make better decisions with AI-powered analytics technologies. Most companies that say they are using AI have yet to gain any value from their investment, according to the 2019 Artificial Intelligence Global Executive Study and Research Report from MIT Sloan Management Review and Boston Consulting Group (BCG). They continue to plug away, however, even though the payoff -- new products, increased revenues and optimized efficiencies -- is likely further out than previously imagined.

Big Data scenarios pose a new challenge to traditional data mining algorithms, since they are not prepared to work with such amount of data. Smart Data refers to data of enough quality to improve the outcome from a data mining algorithm. Existing data mining algorithms unability to handle Big Datasets prevents the transition from Big to Smart Data. Automation in data acquisition that characterizes Big Data also brings some problems, such as differences in data size per class. This will lead classifiers to lean towards the most represented classes. This problem is known as imbalanced data distribution, where one class is underrepresented in the dataset. Ensembles of classifiers are machine learning methods that improve the performance of a single base classifier by the combination of several of them. Ensembles are not exempt from the imbalanced classification problem. To deal with this issue, the ensemble method have to be designed specifically. In this paper, a data preprocessing ensemble for imbalanced Big Data classification is presented, with focus on two-class problems. Experiments carried out in 21 Big Datasets have proved that our ensemble classifier outperforms classic machine learning models with an added data balancing method, such as Random Forests.

As we enter into 2020 and make predictions on what lies ahead in the New Year, it's time to look at how artificial intelligence could advance even more rapidly in the next 12 months. During the past few years, AI garnered a vast amount of global attention and became the most buzzworthy term of the decade as tech's next biggest thing. However, too much speculation led some to believe AI might not live up to its hype, and the workforce is eager to start seeing its potential and tangible results. This past year saw a move toward more practical applications in response to this concern. We saw AI become tangible to the enterprise, providing an efficient and scalable method to gain value from information.

The system also acts as a giant feedback-response or learning loop which learns from the data collected via user/system interactions.

On the 3rd of April 2019, Partech hosted an event on Artificial Intelligence, Open Day #3: Intelligence Artificielle "Fantasmes vs réelles avancées", in collaboration with Innovation Factory. The event which took place at the Partech Shaker, saw several industry experts and exciting new startups share their visions on the future of AI and how it can be implemented in a variety of different use cases. Philippe Colombel, Co-Managing Partner at Partech, introduced the event. He began by underlining how "AI is not new… but accelerating", appearing first in the 1950s and 60s but not really gaining traction until the last decade or so. Phillippe pointed out two revolutions that are driving the new emergence of AI.

The University of South Carolina wants to start working smarter. Later this summer, the school will open an institute dedicated to studying and developing artificial intelligence, which is sometimes abbreviated AI, the school announced Tuesday. The institute aims to use its AI research to help develop "self-improving" and customized programs for social workers, pharmacists, teachers and more, the release said. To do that, "The AI Institute plans to enlist philosophers, ethicists, public policy experts, and lawyers dedicated to exploring the societal impact of AI technology, both the good and the unintended negative outcomes," the release said. "For example, some have expressed concern that autonomous vehicles could soon put tens of thousands of truck drivers out of work."

In the context of Industry 4.0, data management is a key point for decision aid approaches. Large amounts of manufacturing digital data are collected on the shop floor. Their analysis can then require a large amount of computing power. The Big Data issue can be solved by aggregation, generating smart and meaningful data. This paper presents a new knowledge-based multi-level aggregation strategy to support decision making. Manufacturing knowledge is used at each level to design the monitoring criteria or aggregation operators. The proposed approach has been implemented as a demonstrator and successfully applied to a real machining database from the aeronautic industry. Decision Making; Machining; Knowledge based system

Smart data and the energy transition What is smart data and how can we use it to help us through the energy transition? In this episode, we talk to Dr Kirk Borne, Principal Data Scientist and Executive Advisor at Booz Allen Hamilton, about the advancements in technology that are aiding the transformation of the energy sector. In this latest episode, Kirk shares his views on how data is helping to drive energy efficiency and reduce climate change. Kirk explains why it is important to collect data from many different sensors to help us to fully understand the position on climate change; the importance of integration of data sources to break down the silos of information available; and how taking a prescriptive modelling approach will help us to achieve a better outcome for the planet. Finally, we talk to Kirk about AI, in particular Assisted Intelligence, and how humans and machines are working together to filter and understand the significant amount of data available.

It's no secret that modern technologies like AI, machine learning, big data, and advanced analytics are revolutionizing the world today. In business particularly, these platforms have a direct impact on the way things are done. Predictive analytics, for example, not only helps companies learn more about their customers but also provides them with enough info to make informed decisions by revealing outcomes well before they happen. AI and machine learning can be used to automate tedious and repetitive tasks, run support channels via contextual interactions and even operate machinery or robotics. Driverless vehicles -- set to be on our roadways within the next few years -- will be powered almost entirely by AI algorithms.

There is a lot of talk about leveraging Big Data and the value of using it for fraud prevention. What is your take on this? Big Data plays a major role in fraud prevention, however an equal focus should be directed towards how much data is necessary to properly execute the fraud prevention process. It is often said that the more data one adds, the better results one gets. We have also noticed a trend in many marketing messages that claim the same thing: the more data, the better.