Bias in artificial intelligence (AI) systems can occur when the data used to train the model is biased, or when the algorithms used to build the model are biased. Bias in AI can lead to unfair or unequal treatment of certain groups, and it can also undermine the accuracy and reliability of the AI system. There are several steps that can be taken to avoid bias in AI: Use a diverse and representative dataset: The data used to train an AI model should be diverse and representative of the population or problem the model is intended to address. If the data is not representative, the model may make biased or unfair decisions. Preprocess the data: It is important to carefully preprocess the data to ensure that it is clean and consistent.

By Giuliano Liguori 1 Cost Reduction Digital transformation solutions can capture real-time data through IoT devices and analyze the same through AI and ML-powered devices. Talking about the manufacturing sector, it is easy to manage the inventory and monitor critical production processes using digital transformation. Manufacturers need to deploy fewer laborers for less critical production thanks to automation. It results in reduced expenditure and increased productivity. What's more, remote monitoring solutions can enable manufacturing companies to manage inventory in real-time.

The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable. K-NN is a non-parametric algorithm, which means it does not make any assumption on underlying data. It is also called a lazy learner algorithm because it does not learn from the training set immediately instead it stores the dataset and at the time of classification, it performs an action on the dataset. The Naive Bayes classification algorithm is a probabilistic classifier.

In this episode of SaugaTalks, hosted by Irene Lyakovetsky, I met Stu Bailey, Co-founder and Chief Enterprise AI Architect at ModelOp, to discuss why #ModelOps is a Critical Piece of Enterprise AI Strategy. It is not surprising seeing why #ModelOps is the cornerstone of every #AI initiative. Technology Talk Host: SaugaTalks Chats With Fascinating People In Tech! Follow: bit.ly/SaugaTalksLI and Subscribe For The Full Episodes: bit.ly/SaugaTalks "#ModelOps is a Key #Enterprise Capability for End-to-end #Governance of #AI Initiatives Across the Organization" #SaugaTalks with Giuliano Liguori, Digital Transformation Leader, Innovation Manager, Thought Leader, Board Member CIO Club IT and Stu Bailey, Co-founder and Chief Enterprise AI Architect at ModelOp Full Episode: https://lnkd.in/eVAxm_mt

Modern organized enterprises recognize that the adoption of a data-driven strategy is crucial to compete in an increasingly digitalized market. Data and analytics have become a very high priority, rising to the board level, which sees technologies such as Machine Learning and Artificial Intelligence as an opportunity to increase business capabilities, making processes more efficient, and facilitating the spread of new business models. Far and wide, investment in AI and data management are drastically increasing, and new data science projects are underway to build predictive and analytical models for various purposes. However, while companies plan to scale up sophisticated Artificial Intelligence solutions in a reasonable time, the harsh reality is that the adoption of these solutions is often stalled because companies generally focus more on development than on the operationalization of the models. For many non-digital native businesses, the adoption of the data science discipline is often begun with numerous self-contained and fragmented data science teams committed by and large to developing models of Machine Learning and Deep Learning. These small teams of data scientists have sprung up in the varied business units with the aim of building models for different business purposes.

“🔝 #MachineLearning Prediction Algorithms {#infographic} by @DatumGuy Random regression Logistic regression Decision Tree Random forest Gradient Boosting @antgrasso @Ronald_vanLoon @KirkDBorne @SpirosMargaris @mvollmer1 @machinelearnflx @AISOMA_AG @andy_fitze @SwissCognitive”

Verdict lists ten of the most popular tweets on artificial intelligence (AI) in November 2019, based on data from GlobalData's Influencer Platform. The top tweets were chosen from influencers as tracked by GlobalData's Influencer Platform, which is based on a scientific process that works on pre-defined parameters. Influencers are selected after a deep analysis of the influencer's relevance, network strength, engagement, and leading discussions on new and emerging trends. Vala Afshar, Chief Digital Evangelist at Salesforce, shared a video of an interview of Bill Gates at a talk show hosted by David Letterman in 1995. Bill Gates tries to explain the internet to Letterman in the video.