AI News, Machine Learning Becomes Mainstream: How to Increase Your Competitive Advantage

Machine Learning Becomes Mainstream: How to Increase Your Competitive Advantage

First there was big data – extremely large data sets that made it possible to use data analytics to reveal patterns and trends, allowing businesses to improve customer relations and production efficiency.

Then came fast data analytics – the application of big data analytics in real-time to help solve issues with customer relations, security, and other challenges before they became problems.

Now, with machine learning, the concepts of big data and fast data analytics can be used in combination with artificial intelligence (AI) to avoid these problems and challenges in the first place.

With machine learning, professionals and businesses in these industries can get improved performance in a number of areas, including: In their raw data, large and small data sets hide numerous patterns and insights.

Intel has worked relentlessly to develop libraries and reference architectures that not only enable machine learning but allow it to truly take flight and give businesses and organizations the competitive edge they need to succeed.  In fact, according to a recent study by Bain (1), companies that use machine learning and analytics are:

With access to larger and larger data sets, data scientists have made major advances in neural networks, which have led to better accuracy in modeling and analytics.

For statistical/other machine learning, statistical algorithms and algorithms based on other techniques are applied to help machines estimate functions from learned examples.

Not only is the demand for machine learning growing, though, but there is now an evolving ecosystem of software dedicated to furthering machine learning and giving businesses and organizations the benefits of instantaneous, predictive analytics.


Machine Learning

Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.

The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.

Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.

Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.

13 Machine Learning & Data Science Startups from Y Combinator Winter 2016

Note: This is an inspirational post for people who see themselves as data science entrepreneurs in few years from now and aspire to work on a business idea.

If you too have similar question pertaining to future of data science in mind, here you’ll find a good reason to trust.

Almost every industry (I’m not sure about oil and metals), has invested millions of dollars in implementing data driven business methods. These startups will define the future of data science industry.

With this post you’ll learn about upcoming business ideas in machine learning and data science industry.

This software allows companies to establish their presence in all popular messaging platforms and accessing every app from this software.

Protonet help companies to fight against data security using a project management and collaboration software on secure private cloud servers.

It’s multitude of features include task management, file sharing, business communication, mobile collaboration and much more.

It applies predictive modeling to help companies make better budget allocation, target the right set of customers and help save money.

In addition, it also provides dynamic marketing expenditure data, allows cross device tracking and integration with world’s popular apps.

Elucify helps companies to reap the most benefits out of lead data using artificial intelligence and machine learning.

clean old leads, extracts the targeted leads for fast conversion, dynamically search and update lead data and do all this in couple of hours.

It endeavours to make software engineers more productive by providing data based timely feedback and analyzing weekly performance.

This software uses text mining, analyzes the sentiments and decides for a best suited email for a particular personnel.

Using this software, company claims to have improve deliverability (less spam), 3X speed and increases shoots up representative response rate.

This startup provides machine washable clothes which is embedded with sensors to capture a person’s complete workout motion.

Once the data is captured, it delivers specific body insights. Who wondered even the clothes we wear will some day track data ?

This startup has come up with a product which tracks every move in terms of speed, intensity, count and delivers analytical insights.

It provides real time monitoring, reviews performance and helps a player to overcome hurdles quickly.

A data analysis software one can say! With this, they plan to help government and developing countries to improve their administration using their huge sets of generated data.

They provide actionable insights which will help countries to make informed decision making and remove any ambiguity from decision making process.

DeepGram has created an AI enabled tool which builds AI models to automatically analyze and classify the audio/ video streams.

Also, it uses deep learning algorithms to extract speech to text insights relieving humans of manual process.

Out of 120 startups, 13 startups are found to have built product empowered with machine learning and artificial intelligence.

5 Ways Data Science and Machine Learning Impact Business

Data science and machine learning are having profound impacts on business, and are rapidly becoming critical for differentiation and sometimes survival.

With their ability to frame complex business problems as machine learning or operations research problems, data scientists hold the key to unveiling better solutions to old problems.

Data scientists hold the key to unveiling better solutions to old problems One example, popularized by the film and book Moneyball, showed how old ways of evaluating performance in baseball were outperformed by the application of data science.

It achieved this by using analytics to identify high-performing players who other teams had overlooked using traditional methods, and therefore acquired their services at a relatively low cost.

For example, data scientists at a Japanese maritime services provider realized that when providing their traditional services for ship classification, they were collecting a valuable store of data that had great potential in other areas.

Automated analysis of various disease symptoms and medical test data is another common area where the application of data science is already changing lives for the better — or even saving them.

A deeper dive by a data science team can uncover something interesting about what is really happening One recent example is that of Zurich Insurance, which reduced the inefficiencies around handling injury claims by using an artificial intelligence (AI) solution to fully automate injury report assessments.

Common examples include online retailers investigating why customers return goods despite prices being unmatched, deliveries being on time and quality being good, or manufacturers running open investigations into quality fluctuations.

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