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IBM_Watson

IBM Watson: Application of Intellectual computing in Life Sciences Study

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Life Sciences Researchers are under pressure to innovate faster than ever. Big data offer the promise of unlocking unique insights. Today data is of utmost importance in every field ranging from Internet data to data collected from customers. Nowadays we can see that in every shopping malls, cabs drivers, home delivery services, fast food chain stores take feedback from their customers because they wanted to gather data in order to expand their business with the help of data which permanently give them a huge customer database. Although more data are available than ever, only a portion of it is being executed, understand and analyzed.


New technologies like cognitive computing proposed promise for inscribing this dare because cognitive solutions are exclusively designed to integrate and analyze big data sets. Cognitive solutions are designed to understand various datasets in a structured database. Cognitive solutions are structured to understand technical, industry-specific content, advanced reasoning and predictive modeling.

Watson, a cognitive computing technology, has been configured to support life science research. The Watson edition encompasses medical literature, patents, chemical and pharmaceutical data that researchers would commonly use in their work.




Many people know Watson as the IBM-developed cognitive supercomputer that won the Jeopardy game show in 2011. In truth, Watson is not actually a computer but a set of algorithms and APIs. IBM has used in every industry from finance to healthcare.


Recently IBM has announced several new partnerships which focus on taking things further and level extent. Also, it puts its cognitive capabilities to solve a new set of technologies around the world.


Meaning of Cognitive computing as per the Vice President of IBM Watson Steve Gold, what started with his own company’s development of tabulation computing, to process US census data at the inception of the 20th century developed into programmatic computing in the middle of the century, with the arrival of transistors, relational databases, magnetic storage and microprocessors.
We can see the tremendous growth in unstructured data we have faced in recent past years, the artificial methods that have been developed to help us make sense and learn from this data have given rise to cognitive computing. These cognitive computers don’t need to be programmed- they can learn from themselves.


The concept works like traditionally, computers have done what we tell them to do. We give them command and they need to follow that command as computers were already programmed to follow commands. We give them code containing instructions whatever we want them to be done and they follow the orders and carry out the result in the same fashion.
There is a limitation that they can only do whatever we want them to do things and what we teach them to do. We can’t simply order computers to develop a remedy to cure AIDS and expects the results for the same. But considering the fact that the computers can process the data and information far faster than a human can do. It will be much efficient to give them all the data available and let them work out the best solution for it.

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Artificial Intelligence

3 Driving Factors of Artificial Intelligence

Artificial Intelligence is no doubt the next generation technology everyone is looking forward to. China who wants to be the world leader in Artificial Intelligence has added AI in the school curriculum of high-school students. Now, you can imagine the importance of AI in the coming future.

AI has already put his legs on various verticals of the industry including automobile, healthcare, finance, manufacturing and retail to name a few. From robotic surgery to self-driving cars, AI has proven its implications on each and every application. But what actually is driving the Artificial Intelligence?

Well, companies like Amazon, Facebook, Apple, Google, IBM as well as Microsoft are investing in the research and development of AI. However, there are 3 factors which are accelerating the growth of Artificial Intelligent.

Next-Generation Computing Architecture (GPU)

Machine Learning and Artificial Intelligence require speedy processors. The traditional microprocessors and CPUs are not meant for Machine Learning. They require a new breed of processors. This made the rise of GPU. The Graphics Processing Units which used to be the part of high-end gaming PC’s and workstations have seen a tremendous growth after the evolution of AI.

GPU’s come with numerous cores that speed up the ML training process. Seeing the growth of AI, it might happen that down the line each CPU will come paired-up with a GPU.

Availability of Cloud

Data Scientists require large data sets and historic data for ML models to predict with more accuracy. In order to predict the weather or solve problems like detecting cancers, data scientists need a large amount of data to analyze. The efficiency of ML models depends merely on the size of data. The more the data, the better is the accuracy.

With the use of cloud and businesses, government and academia (the major data holders) unlocking their data on cloud, helped the Artificial Intelligence to grow by leaps and bounds.

Advancement in Deep Learning



Artificial Neural Network is replacing the conventional Machine Learning system. The advancement in Machine Learning Models with the new technologies like Capsule Neural Network and Transfer Learning have changes the way ML models are trained and implemented. These techniques are so advanced that they are able to produce the most accurate results with a limited data.

Conclusion

Various companies like IBM through IBM Watson, Microsoft with Corona, Amazon with its Alexa and major players like China are investing billions in Artificial Intelligence to make the world mobile. With selecting the clothes to turning off lights through voice and self driving cars, they want the automation to rule the world by 2030.

 

 


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10 Companies using IBM Watson to drive their Sales

10 Companies using IBM Watson to drive their Sales

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IBM Watson is a supercomputer integrated with cognitive technologies. It provides cloud-based predictive analytics for business insight. It has the capabilities, through which you can accelerate research and discovery, enrich your interactions, detect liabilities and mitigate risks.

Here is a list of 10 companies who are using IBM Watson in different ways to enhance their capabilities through Artificial Intelligence and Machine Learning.

Staples

staples

The global stationary retailer Staples has created a smart ordering button using IBM Watson’s Machine Learning capabilities and other cognitive APIs.

This smart button is placed in Staple’s office which let the users to order smartly. This button uses voice recognition capabilities for ordering. For example you can say ‘order 100 blue pens’ to make an order for the same.

Wimbledon

wimbledon

Wimbledon has used IBM Watson platform to generate automated video highlights. This has eliminated the task of video editor who used to quickly edit and cut the videos to put a highlight package.

Here, IBM Watson engine pulls in information to create video highlights based on crowd noise, social traction, facial recognition and sentiment analysis of players.

Rocket Fuel

rocket-fuel

Rocket Fuel founded in 2008 started working with IBM Watson to allow for brand’s to make sure that the ads aren’t served against the critical or negative content.

It also integrated Watson’s Discovery service which allows sentiment tracking. This service helps Rocket Fuel to understand whether someone should place their advertisements on that page or not.

General Motors

General-motors

General Motors joined the hands with IBM to integrate cognitive technologies to GM’s cars. It offers location-based products and services to you while you’re in GM’s car.

GM’s OnStar Go is automobile industry’s first intelligent mobility service. It uses machine learning to understand users’ preferences. Based on which customers will receive personalised marketing services from a number of GM’s partners.

Conde Nast

Conde nast

Conde Nast uses IBM Watson to help build informed social media campaigns. The software build by IBM offers Conde Nast customers such as New Yorker and Vogue insight into whom to target their campaigns and which celebrity would make good brand ambassador.

Using this software, if a brand wants to find an influencer who is kind, Watson will analyse at least 20,000 words and emojis the potential ‘influencer’ have published.

Macy’s On-Call

Macy's

US retailer Macy’s in partnership with Satisfi, introduced ‘Macy’s On-Call’ service through which the shoppers can ask Watson questions about store’s products, services and facilities.

This software powered by IBM Watson, helped them to engage one-on-one with customers, providing them another level of service right at their fingertips.

American Cancer Society

American-Cancer-society

American Cancer Society partnered with IBM Watson to leverage the patients with personalised information and advices regarding to their illness.

The virtual advisor looks for the patient’s type of cancer, its stage and history to give proper advice. Its machine learning capability enables patient to ask question in natural language and receive audio responses.

Imperial College London

Imperial-College

Imperial College London used Watson to analyse and predict crime. The computing students in Imperial College of London worked with IBM and team Watson to develop an application which could be able to solve challenging problems within the university’s crime department.

CitiGroup

Citigroup

The multinational company, Citigroup uses IBM Watson to improve its customer relationships and interactions within the banks.

Citigroup uses Watson to collect the information/feedback from customers and provide suggestions to improve interactions, evaluate risks, and identify opportunities.

Standard Bank

Standard-bank

Standard Bank is using Watson to speed up their client handling. Watson helped the bank to speedily handle the customer queries so that they can be responded in faster time.

This would help them with better customer service and this would eventually retain their loyalties and bring them new customers.