ML Archives - Liwaiwai

Using ML To Predict The Weather And Climate Risk

Introduction All beings on this planet are affected by atmosphericphenomena we call the weather. Because of this, humans have invented all sorts of measuring tools, and luckily have loads of data from observations. All this data has been on quite a technological journey; from being collected on paper and local servers in basements, to now living on open cloud platforms that normalize all the different sensor data for anyone to research. AI techniques to build improved predictive models have also made way using neural networks, called deep learning. My colleagues in Google Research who study AI Weather & Climate, built…

Sentiment Analysis With BigQuery ML

Introduction We recently announced BigQuery support for sparse features which help users to store and process the sparse features efficiently while working with them. That functionality enables users to represent sparse tensors and train machine learning models directly in the BigQuery environment. Being able to represent sparse tensors is a useful feature because sparse tensors are used extensively in encoding schemes like TF-IDF as part of data pre-processing in NLP applications and for pre-processing images with a lot of dark pixels in computer vision applications. There are numerous applications of sparse features such as text generation and sentiment analysis. In…

Built With BigQuery And Google AI: How Glean Enhances Enterprise Search Quality And Relevance For Teams

Context AboutGlean Glean searches across all your company’s apps to help you find exactly what you need and discover the information you need to do your best work. It delivers powerful, unified enterprise search across all workplace applications, websites, and data sources used within an enterprise. Search results respect the existing permissions from your company’s systems, so users only see what they already have permission to see. Glean’s enterprise search also takes into account your role, projects, collaborators, and the language and acronyms specific to your company to deliver highly personalized results that provide you with information most pertinent to…

8 Best Human Behaviour Datasets For Machine Learning

Human behaviour describes how people behave and interact. It is based on and affected by a number of elements, including genetic makeup, culture, personal values and attitudes. In this article, we will look at the 8 Best Human Behaviour Datasets for Machine Learning. List of the Best Human Behaviour Datasets Common Use Cases for Human Behaviour Datasets Economic and Social Development The World Bank’s World Development Indicators dataset – Common use cases of this dataset include: Demographics and Economic Trends The U.S. Census Bureau’s American Community Survey dataset – Common use cases of this dataset include: Well-being and Quality of Life The Gallup…

Debunking 4 Common Myths About Machine Learning

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a specific task through experience. The experience matters here. It is an increasingly important field with a wide range of applications, from image and speech recognition to natural language processing and decision-making. So, nowadays we can do anything using machine learning as long as we have data available for the job at hand. One of the key advantages of machine learning is its ability to automatically improve and adapt to new data. This allows it…

The Term AI Overpromises. Let’s Make Machine Learning Work Better For Humans Instead

• Current ‘artificial intelligence’ operates on a limited ‘monkey see monkey do’ basis. • There is little sign such systems can make the leap to higher forms of cognition. • We should concentrate our efforts into better integrating machine learning with human operators. One of the popular memes in literature, movies and tech journalism is that man’s creation will rise and destroy it. Lately, this has taken the form of a fear of AI becoming omnipotent, rising up and annihilating mankind. The economy has jumped on the AI bandwagon; for a certain period, if you did not have “AI” in…

7 Challenges In Marketing AI & Machine Learning Solutions

These are interesting times indeed! Almost every other technology-driven company is barging into Artificial Intelligence (AI) and Machine Learning (ML). Yes, even a layman contains a tremendous curiosity to know about AI technology. AI technology could potentially disrupt everything that we do in our daily lives. It is that exceptional and brilliant. Integration of AI & ML into the business ecosystem would give numerous transformation opportunities to leverage the value chain. Accenture states that AI can increase business productivity by a staggering rate, up to 40%. Then why is it the case that businesses find it extremely difficult to market their…

The Pain Points Of Scaling Data Science

While building a machine learning model, data scaling in machine learning is the most significant element through data pre-processing. Scaling may recognize the difference between a model of poor machine learning and a stronger one. Machine learning algorithm only recognizes numerical if there is a significant difference in the dimension, say few varying in tens or hundreds or often in thousands, among these predominant numbers when the data is used before scaling, it attempts to play a more significant role while preparing the ML model. For machine learning algorithms, data scaling is important in calculating intervals between data and evaluating…

Fight Fire With Fire: Using Good AI To Combat Bad AI

Real-world cases and expert opinions about the present and future of audio deepfakes and AI-powered fraud in particular and how to use AI to build a strong defense against malicious AI. John Dow, the CEO of an unnamed UK-based energy firm, once got a call from his boss that he wishes he’d never answered. Being confident he was talking to the CEO of the firm’s German parent company, he followed the instruction to immediately transfer €220,000 (app. $243,000) to a Hungarian supplier’s bank account. Having completed a transaction, John got another call from the boss confirming the reimbursement. However, he noticed that…

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