1． Understanding Artificial Intelligence Trends in 2018
The level of technological advancement and adoption of Artificial Intelligence in different business industries is incredible. Every firm is striving to integrate machine learning and smart services to enhance and boost production. There is more to business in 2018 than the conventional marketing techniques; apart from e-Commerce where most transactions and purchases are made online, AI is rising in all nature of businesses. Top online business companies are pushing for the implementation of different aspects of AI in business.
In 2017, the hype was more on Deep Learning and Machine Learning. The year goals and rise in technology were beyond expectation; the fact that international business is the trend and expectation for success in any niche, all firms are investing and adopting some technology. Also, firms were striving to reduce expenses concerning wages and salaries by replacing employees with machines.
In 2018, here are some of the AI trends to read:
Moving towards automated machine learning
Machine learning in cloud computing
Deep learning leading
Hybrid learning models
Black Box Solution
2． Big Data Meets Artificial Intelligence to Create New Possibilities
Big data and Artificial Intelligence (AI) are innovative technologies in their own right. By combining big data with artificial intelligence, we can analyze and monitor large data sets in unique and unexplored ways.
AI is the simulation of human intelligence by smart computers. By applying machine learning algorithms, we can make ‘intelligent’ machines, which can employ cognitive reasoning to make decisions based on the data fed to them. Big data, on the other hand, refers to computational strategies and techniques applied to large sets of data to mine information from them. Big data technology includes capturing and storing data, analyzing it to make strategic decisions and improve business outcomes. Most companies deploy big data and AI in silos to structure their existing data sets and to develop machines which can think for themselves. But, big data is in reality the raw material for AI. So, when big data meets AI, they have the potential to transform both, the way data is structured and the way machines learn.
3． How Artificial Intelligence Is Taking Over Oil and Gas
Artificial intelligence, or rather things like machine learning and automation, which are often wrongly called artificial intelligence, is a big thing in oil and gas right now. The hype around AI spreads a lot further than the oil and gas industry, but in it, the technology is making the first splashes and it looks like they are fast multiplying. While “AI” — or more accurately predictive and analytic algorithms, and automation — in the upstream segment of the industry has garnered some attention already, there is a somewhat surprising part of the oil and gas industry that may be as ripe as exploration and production for some software help: permitting and environmental assessment. Researchers from the Environmental Defense Fund are working on a system using Natural Language Processing that could streamline what is now a very complex process to the benefit of all stakeholders involved.
4． AI and Machine Learning for Small Businesses: Are They Worth the Investment?
AI and machine learning continue to open new opportunities in the business world, but many small businesses don’t think they can benefit from such technologies. Despite the increasing availability and capability of machine-based intelligence, the price often seems to leave it in the hands of large corporations. However, small firms increasingly have a chance to cash in. Are AI and machine learning worthwhile investments for your business? The following information can help you decide:
Modeling and forecasting
5． Catch the Next Wave of Digital IT Transformation: Learning Tech
With the rise of the digital economy, businesses are redefining themselves in part or wholly as software companies. While IT has been part of business for decades, its role has changed. Data and technology are now core elements of modern business strategy. New digital initiatives, often categorized as digital transformation, aren’t simply about using data to run your business. Digital IT transformation is about using data and IT services to gain a competitive advantage.
6． Artificial Intelligence can Assess Personality
A new artificial intelligence driven platform can assess a person’s personality by scanning the eyes. This is through assessing the way that the eyes move, through the use of an advanced tracker and data analysis software.
Putting aside the concept of ‘personality’ and its disputed nature within psychology, the new platform attempts to group subjects into different personality types on the basis of eye-motion. The research comes from the University of South Australia.
Visual exploration is driven by two main factors. First there is the stimuli in our environment; and second, in response to our own individual interests and intentions. It is through the latter that some researchers think that personality traits can be discerned.
Personality traits, or trait theory, is a theoretical approach to the study of human personality, focused on measurement of traits, which are defined as habitual patterns of behavior, thought, and emotion. The most widely accepted personality traits are the so-called ‘Big Five’, namely: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism.
7． How to Reduce Variance in a Final Machine Learning Model
A final machine learning model is one trained on all available data and is then used to make predictions on new data. A problem with most final models is that they suffer variance in their predictions. This means that each time you fit a model, you get a slightly different set of parameters that in turn will make slightly different predictions. Sometimes more and sometimes less skillful than what you expected. This can be frustrating, especially when you are looking to deploy a model into an operational environment.
8． What Are Machine Learning Models Hiding?
Machine learning is eating the world. The abundance of training data has helped ML achieve amazing results for object recognition, natural language processing, predictive analytics, and all manner of other tasks. Much of this training data is very sensitive, including personal photos, search queries, location traces, and health-care records. In a recent series of papers, we uncovered multiple privacy and integrity problems in today’s ML pipelines, especially online services such as Amazon ML and Google Prediction API that create ML models on demand for non-expert users, and federated learning, also known as collaborative learning, that lets multiple users create a joint ML model while keeping their data private (imagine millions of smartphones jointly training a predictive keyboard on users’ typed messages).
9． What Machine Learning Can and Cannot Do
Machine learning systems are not equally suitable for all tasks. It’s been most successful when applied with supervised learning and deep learning algorithms, which require very large amounts of carefully labelled data to be used for training — e.g. cat, not-cat. While very effective in such domains, ML systems are significantly narrower and more specialized than humans. There are many tasks for which they’re completely ineffective given the current state-of-the-art.
Brynjolfsson and Mitchell identify eight key criteria that help distinguish tasks that are suitable for ML, from those where ML is less likely to be successful.
Learning a function that maps well-defined inputs to well-defined outputs.
Large (digital) data sets exist or can be created containing input-output pairs.
The task provides clear feedback with clearly definable goals and metrics.
No long chains of logic or reasoning that depend on diverse background knowledge or common sense.
No need for detailed explanation of how the decision was made.
A tolerance for error and no need for provably correct or optimal solutions.
The phenomenon or function being learned should not change rapidly over time.
No specialized dexterity, physical skills, or mobility required.
10． Data Science to Analyze Big Genomic Data
The main objective of this project is to identify new stem cell populations in mouse brain. Characterizing the unique gene expression signature of these cells could be the starting point for finding and defining Cancer Stem Cells (CSC) in human tumors. In particular, glioblastoma, which is the most common malignant brain tumor diagnosed in adults. Despite numerous advances in cancer therapy, these tumors remain incurable, suggesting that current treatments fail to target the cells responsible for tumor growth. One possible explanation for this would be that CSCs are resistant to current therapies and responsible for tumor remission.