ATABKAM Professional Services - Delivering Business Value Consistently
RSS Follow Become a Fan

Delivered by FeedBurner


Recent Posts

How exercise may improve the immune system’s ability to fight cancer
Jack Dorsey: Square, Cryptocurrency, and Artificial Intelligence | AI Podcast #91
The Question I Almost Didn't Ask And How It Changed My Life
Face Masks for All - #masks4all | Slowing Spread of Coronavirus Infection with Homemade DIY Masks
MIT’s deep learning found an antibiotic for a germ nothing else could kill

Categories

Artificial Intelligence
Business Process Management (BPM)
Business Transformation
Celebrations
Change Management
Climate Change
COVID-19
Economics
Entrepreneurship
General Management
Healthcare
Leadership
New Technologies and Innovation
Project / Program Management
powered by

atabkamprofessionalservices

New Technologies and Innovation

Machine Learning Is The Future Of Cancer Prediction



*********
Every year, Pathologists diagnose 14 million new patients with cancer around the world. That’s millions of people who’ll face years of uncertainty.
Pathologists have been performing cancer diagnoses and prognoses for decades. Most pathologists have a 96–98% success rate for diagnosing cancer. They’re pretty good at that part.
The problem comes in the next part. According to the Oslo University Hospital, the accuracy of prognoses is only 60% for pathologists. A prognosis is the part of a biopsy that comes after cancer has been diagnosed, it is predicting the development of the disease.

How is machine learning affecting genetic testing?

 
*******
Machine learning is being applied to genetic testing in many different ways.
The applications are nearly endless. Machine learning is helping scientists to analyze DNA, decode the human genome, assess disease phenotypes, understand gene expression, and even participate in a process called gene editing, where DNA is actually “spliced” into an organism’s genetic code...
*******    
  
(To read the entire article, please click on the image below)























Data Scientists: The New Rock Stars of the Tech World



*****
Thedata scientistrole is fast becoming the most sought-after career in the technology world. Companies like Google, Facebook, Amazon and LinkedIn are using data scientists to help them maintain that innovative edge in the digital data era. And now data and technology enthusiasts are aspiring to become data scientists the same way some musicians aspire to become rock stars. Perhaps that's why some people are referring to data scientists as the new rock stars of the technology era.

Unfortunately, this role is still so new that there's still a level of obscurity about it, which means many wannabe data scientists are driving their tour buses down the wrong road.

A Friendly Introduction to Machine Learning


(To watch the video, please click on the image below)




















Robot co-workers? 7 cool technologies changing the way we work



******
Technology is poised to change the workplace. Soon you may have a robot for a co-worker or a microchip embedded under your skin that's a work ID.

Some innovations are already making an impact. Virtual reality, for example, is going beyond gaming to serve as a powerful workplace training tool.

One of the biggest areas where VR training can be useful is safety, according to J. P. Gownder, vice president at research firm Forrester.

A startup named Strivr designs VR experiences that allow construction workers who put on a VR headset to understand hazards before entering a site.

5 Ways to Help Employees Keep Up with Digital Transformation


 
******
The consumer packaged goods (CPG) landscape is in the midst of a significant shake-up. Coca-Cola recently reshuffled its leadership team to focus on growth, innovation, and digital. Unilever has acquired Dollar Shave Club, a young startup, for $1 billion in a move to introduce a new model of subscription sales. L’Oréal has made a strategic investment in Founders Factory, a digital startup accelerator. And at Greycroft, a venture capital firm, investor Teddy Citrin has laid out a 

On machine learning and structure for (driverless cars) mobile robots




******
Due to recent advances - compute, data, models - the role of learning in autonomous systems has expanded significantly, rendering new applications possible for the first time. While some of the most significant benefits are obtained in the perception modules of the software stack, other aspects continue to rely on known manual procedures based on prior knowledge on geometry, dynamics, kinematics etc. Nonetheless, learning gains relevance in these modules when data collection and curation become easier than manual rule design.

30+ Real Examples Of Blockchain Technology In Practice



*****
While Bitcoin and cryptocurrency may have been the first widely known uses of blockchain technology, today, it’s far from the only one. In fact,blockchainis revolutionizing most every industry. Here are just a few of the practical examples of blockchain technology.

Entertainment

KickCity—Platform for event organizers that enables them to pay only for what they get, and rewards community members by sharing those events. Their products generate around $50k monthly with more than 70k users and 300 event hosts.

5 Neural Network Use Cases That Will Help You Understand the Technology Better



*****
Every day, highly advanced artificial neural networks (ANNs) and deep learningalgorithms scan through millions of queries and dig through the endless flow of big data. They are providing the knowledge required to fuel the many ever-evolving artificial intelligence (AI) that many software houses have incorporated in their products. Machine learning is the instrument through which these newborn computer-based bits of synthetic intelligence process all the info they're nourished with, much like the five senses help a human toddler learn and experience the world.

How Machine Learning Can Improve Supply Chain Efficiency



********
Takeaway:  In order for a business to succeed, it must have a properly managed supply chain. Machine learning is helping to improve the accuracy and efficiency of supply chain management.

In today's volatile and complex business world, it is very difficult to make a reliable demand forecasting model for supply chains. Most forecasting techniques produce disappointing results. The root causes behind these errors are often found to be lying in the techniques that are used in the old models.