TOP NEWS

 

“NEWS ON MACHINE LEARNING”

• 17 Ideas For Using Machine Learning In Communications…..

  

Machine learning and artificial intelligence have multiple applications in various fields. Communications and marketing is no exception. Just like other industries, it can help improve how smoothly everything runs and offer additional insights that were difficult to obtain manually.  We asked members of Forbes Communications Council for ideas on how machine learning and artificial intelligence can help marketing and communications.

• Apple’s Latest Silicon Doubles Down on Machine Learning

Last week, Apple announced that its latest line of smartphones would contain a 6.9 billion-transistor chip based on 7-nanometer manufacturing technology, the most advanced process available. But the performance of the new processor, the A12, highlights the company’s increasing focus on neural-network and graphics-computing as the advantages of scorching smaller and smaller transistors onto silicon wafers wind down.

• What An Experienced Machine Learning Engineer Knows That A Beginner Doesn’t..

How does one evaluate experienced machine learning engineers as opposed to beginners? Overall, the role of machine learning engineers is working primarily on building data pipelines, A/B test and benchmarking infrastructure. The job of ML engineer is to get the inputs one requires to feed the models. Only when this process is done, that the data scientists focus on building the actual algorithms or models. Here, they use industry standard tools such as logistic regression, random forest, sometimes other linear models. According to one Redditor, the data pipeline is similar to a logistic model and runs in batches once a week.

• DeepFakes Is One Of The Strangest Machine Learning Algorithms Ever. Here’s Why

Every advancement in technology has the good and the bad side. In one of our earlier articles, we had discussed how DeepFakes software created a controversial stir where miscreants rampantly created fake pornography videos by swapping faces of celebrities, not to mention false propaganda of politicians again through videos. It clearly showed how bad a technological advancement can go in an online world.

• THIS ROOM USES MACHINE LEARNING, AI TO CHANGE SHAPE IN RESPONSE TO HUMAN BEHAVIOUR

  The approximately six-to-nine square metre pavilion explores three main areas, machine learning and artificial intelligence; digital fabrication and robot fabrication; and augmented reality and virtual reality.

  This will profoundly change the way architects design, develop and manufacture in the future, the researchers said.

   

• Machine-learning system tackles speech and object recognition, all at once..

MIT computer scientists have developed a system that learns to identify objects within an image, based on a spoken description of the image. Given an image and an audio caption, the model will highlight in real-time the relevant regions of the image being described.

Unlike current speech-recognition technologies, the model doesn’t require manual transcriptions and annotations of the examples it’s trained on.

• More efficient security for cloud-based machine learning

• A novel encryption method devised by MIT researchers secures data used in online neural networks, without dramatically slowing their runtimes. This approach holds promise for using cloud-based neural networks for medical-image analysis and other applications that use sensitive data.Outsourcing machine learning is a rising trend in industry. Major tech firms have launched cloud platforms that conduct computation-heavy tasks, such as, say, running data through a convolutional neural network (CNN) for image classification.

• ‘Machine Learning for Dummies’ Platform Mate Labs Raises $550K from Omphalos Ventures

  

  Bangalore-based Mate Labs, an artificial intelligence (AI) based startup that focuses on providing easy solutions for non-developers and businesses, has announced raising $550,000 in pre series-A funding led by Omphalos Ventures India. The other investors who participated in this round include Eagle 10 Ventures, 91Springboard, Deepak Sharma (ex-Chairman of Citi Bank), Khattar Holding, Sumit Sinha and Tejan Timblo among others.

  Founded in 2016, by Kailash Ahirwar and Rahul Vishwakarma, Mate Labs aims to democratise Machine Learning and Deep Learning by making it available for one and all irrespective of whether a user knows how to code or not

• FACEBOOK is using unsupervised ML for translation.

 

In 2014, Facebook have machine learning algorithm which connects people all over the globe. And also FAIR (Facebook AI Research) collaborates with (applied) ML to translate 100,000 languages into another.  And it also shut down its text based Chabot transition, earlier this year so the transition is hardly seamless as viable application for these technologies are sort.

• Machine Learning is more than Games

By now, how many you have even heard about the book by Michael Lewis (The art of winning the unfair game) or have you seen movie that starred Bratt Pitt, or even done business case Billy Beane,

General Manager of Oakland A’s. They used big data in baseball.

Similarly, last five years or so, ML has emerged as a substantial area of global research and considerable investment.

In 1997, IBM’s DeepBlue become first chess program to defeat a reigning world champion GARY KASPAROV due to ML.

In 2011, IBM’s Waston beat the reigning world champion in quiz game JEOPARDY!

In 2017, DeepMind’s ALPHAGO become the first GO playing program to defeat the reigning champion KE JIE.A

• Databricks and Snowflake partner to bring Machine Learning Smart to data warehouse.

Snowflakes is a cloud based data ware housing platform which is given traditional database vendors a run for their own money. And Databricks is a big data company that offers a commercial version of Apache spark on mainstream public cloud platforms in AWS AND AZURE.

THE both company work together on algorithms of ML and data retrieved    from warehouse and store back to snowflake.

•  ML enables physic inspired metrics for analysing art.

An International Research collaborative reports that a systematised AI analysis of artwork produced over the last millennium yields revealing information about historical evolutionary artistic trends.

In National Academy of Science research analyzed a dataset of 137,564 pieces of visual art(mostly painting) hosted by the online encyclopedia wiki art. It contain 2000 artist over 100 styles.

• Success of ML hinges on addressing safety concerns.

“Making algorithms robust to concerted attacks has become priority research area.”

Face recognition is one of the safety concerns which is evolving more due to ML.

Due to ML face recognition is 97% accurate.

“Above news and research on Machine Learning shows the both evangelists and sceptics who debate whether within in 20 years ML algorithm will have replaced may jobs worldwide and will be capable of any work human can do.”

• Is Machine Learning Right for You?

Are you considering using machine learning? First, you have to think about the problem you are trying to solve.

Speaking at the recent Code PaLOUsa conference in Louisville, Brian Korzynski, a senior application developer at United Shore, explained why it’s essential that businesses understand the pros and cons of machine learning.