Apple-picking robots, machine learning ag tech could transform food … – Markets Insider

Markets InsiderApple-picking robots, machine learning ag tech could transform food …Markets InsiderFrom apple-picking robots, to machine learning and wearable tech — these companies are changing the way we grow our food.and more »


Markets Insider

Apple-picking robots, machine learning ag tech could transform food ...
Markets Insider
From apple-picking robots, to machine learning and wearable tech — these companies are changing the way we grow our food.

and more »

5 Innovative Uses for Machine Learning – Entrepreneur

Entrepreneur5 Innovative Uses for Machine LearningEntrepreneurThe answer is that, in the broadest sense, machine learning models are an application of AI in which algorithms independently predict outcomes. In other words, these models can process large…


Entrepreneur

5 Innovative Uses for Machine Learning
Entrepreneur
The answer is that, in the broadest sense, machine learning models are an application of AI in which algorithms independently predict outcomes. In other words, these models can process large data sets, extract insights and make accurate predictions ...

Machine Learning Can Help B2B Firms Learn More About Their Customers – Harvard Business Review

Harvard Business ReviewMachine Learning Can Help B2B Firms Learn More About Their CustomersHarvard Business ReviewNeural networks and “deep learning” algorithms along with other machine learning methods enable data scientists to mine the gold in digita…


Harvard Business Review

Machine Learning Can Help B2B Firms Learn More About Their Customers
Harvard Business Review
Neural networks and “deep learning” algorithms along with other machine learning methods enable data scientists to mine the gold in digital formats. These AI-based methods involve advanced search techniques that identify, categorize, and gather user ...

Here Are 10 Artificial Intelligence Trends to Watch in 2018

Artificial intelligence (AI) is the new technological frontier over which companies and countries are vying for control. According to a recent report from McKinsey, Alphabet invested roughly $30 billion in developing AI technologies. Baidu, which is the Chinese equivalent of Alphabet, invested $20 billion in AI last year. Companies aren’t the only ones investing time, money and […]

Artificial intelligence (AI) is the new technological frontier over which companies and countries are vying for control. According to a recent report from McKinsey, Alphabet invested roughly $30 billion in developing AI technologies. Baidu, which is the Chinese equivalent of Alphabet, invested $20 billion in AI last year.

Companies aren’t the only ones investing time, money and energy into advancing AI technology — a recent article in The New Yorker reported that the Chinese government has been pursuing AI technology aggressively in an attempt to control a future cornerstone innovation.

Considering that some of the largest entities in the world are focused on advancing AI tech, it is all but certain that 2018 will see significant advancements in the space. The following are ten AI trends to look out for this year.

1. AI will become a political talking point.

While AI may help create jobs, it will also cause some individuals to lose work. For example, Goldman Sachs expects self-driving vehicles will cause 25,000 truckers to lose their jobs each month, as reported by CNBC.

Likewise, if large warehouses can operate with just a few dozen people, many of the 1 million pickers and packers currently working in U.S. warehouses could be out of a job.

During the 2016 election, President Trump focused on globalization and immigration as causes of American job-loss, but during the 2018 midterm elections, the narrative could be about automation and artificial intelligence, as more working-class Americans struggle to adjust to the new landscape.

2. Logistics will become increasingly efficient.

We are entering a world in which it will be possible to run a 20,000-square-foot distribution center with a skeleton crew. Companies like Kiva Systems — now Amazon Robotics — use a combination of artificial intelligence and advanced robotics to provide big box retailers with unprecedented logistics solutions.

Warehouses of the future will look nothing like they do today — rather than being designed to accommodate human packers, they will be built for highly capable robots that can work 24/7 and don’t require lighting to see what they are doing.

Kiva Systems, which was purchased by Amazon for $775 million in 2012, creates learning robots that can efficiently find and transport items in Amazon’s warehouses. The technology is already being used today and is expected to play an increasingly prominent role in the company’s quest for faster, less expensive deliveries.

3. Mainstream auto manufacturers will launch self-driving cars.

Tesla was one of the first auto makers to launch a self-driving vehicle. In their effort to keep pace with Tesla, traditional automakers like Audi are poised to release their own self-driving cars in 2018.

The Audi A8 will feature self-driving technology capable of safely shuttling humans without driver input. Cadillac and Volvo are also developing advanced self-driving technology, which will become increasingly visible in 2018.

Read the source article at Entrepreneur.

Here is a Non-Technical Introduction to Machine Learning

Machine learning is a field that threatens to both augment and undermine exactly what it means to be human, and it’s becoming increasingly important that you—yes, you—actually understand it. I don’t think you should need to have a technical background to know what machine learning is or how it’s done. Too much of the discussion about […]

Machine learning is a field that threatens to both augment and undermine exactly what it means to be human, and it’s becoming increasingly important that you—yes, you—actually understand it.

I don’t think you should need to have a technical background to know what machine learning is or how it’s done. Too much of the discussion about this field is either too technical or too uninformed, and, through this blog, I hope to level the playing field.

This is for smart, ambitious people who want to know more about machine learning but who don’t care about the esoteric statistical and computational details underlying the field. You don’t need to know any math, statistics, or computer science to read and understand it.

By the end of this post, you’ll:

  1. Understand the basic logical framework of machine learning (ML).
  2. Be able to define important relevant terms and concepts that anyone interested in this field should know. These terms are highlighted in boldface.
  3. Know which high-level decisions go into building statistical models, and understand some of the implications of these decisions.
  4. Be able to better analyze the question of when we should use the results of ML to make big decisions, such as determining public policy.

This overview is in no way comprehensive. Huge portions of the field are left out, either because they are too rare to merit study by non-technical decision makers, because they’re difficult to explain, or both.

What is machine learning?

The field itself: ML is a field of study which harnesses principles of computer science and statistics to create statistical models. These models are generally used to do two things:

  1. Prediction: make predictions about the future based on data about the past
  2. Inference: discover patterns in data

Difference between ML and AI: There is no universally agreed upon distinction between ML and artificial intelligence (AI). AI usually concentrates on programming computers to make decisions (based on ML models and sets of logical rules), whereas ML focuses more on making predictions about the future.

They are highly interconnected fields, and, for most non-technical purposes, they are the same.

What’s a statistical model?

Models: Teaching a computer to make predictions involves feeding data into machine learning models, which are representations of how the world supposedly works. If I tell a statistical model that the world works a certain way (say, for example, that taller people make more money than shorter people), then this model can then tell me who it thinks will make more money, between Cathy, who is 5’2”, and Jill, who is 5’9”.

Read the source article at the SafeGraph blog. 

How SAP Is Utilizing Machine Learning For Its Enterprise Applications – Forbes


Forbes

How SAP Is Utilizing Machine Learning For Its Enterprise Applications
Forbes
Markus Noga, SAP’s Head of Machine Learning, told me that SAP Leonardo was introduced on January 11, 2017 and was extended to include SAP’s entire digitization efforts with a machine learning foundation at SAP’s SAPPHIRE event in May of 2017. “SAP


Forbes

How SAP Is Utilizing Machine Learning For Its Enterprise Applications
Forbes
Markus Noga, SAP's Head of Machine Learning, told me that SAP Leonardo was introduced on January 11, 2017 and was extended to include SAP's entire digitization efforts with a machine learning foundation at SAP's SAPPHIRE event in May of 2017. “SAP ...

Machine Learning and Artificial Intelligence – Two Conferences to Attend in 2018 – InfoQ.com

Machine Learning and Artificial Intelligence – Two Conferences to Attend in 2018InfoQ.comFor more in-depth exploration on deep learning and the trends around AI and ML, two upcoming software conferences hosted by InfoQ feature deep learning, innovative…


Machine Learning and Artificial Intelligence - Two Conferences to Attend in 2018
InfoQ.com
For more in-depth exploration on deep learning and the trends around AI and ML, two upcoming software conferences hosted by InfoQ feature deep learning, innovative advances such as self driving car architecture, and feature extraction for machine ...

Machine Learning And Behavioral Biometrics: A Match Made In Heaven – Forbes

ForbesMachine Learning And Behavioral Biometrics: A Match Made In HeavenForbesIn the world of behavioral biometrics, machine learning, deep learning and artificial intelligence are all hand-in-glove. Behavioral biometrics identifies people by how they …


Forbes

Machine Learning And Behavioral Biometrics: A Match Made In Heaven
Forbes
In the world of behavioral biometrics, machine learning, deep learning and artificial intelligence are all hand-in-glove. Behavioral biometrics identifies people by how they interact with devices and online applications. As opposed to something that ...

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Machine Learning Can Decipher Data – Medical Marketing and Media


Medical Marketing and Media

Machine Learning Can Decipher Data
Medical Marketing and Media
As researchers and analytics, we interact with a wide array of market research pros in the health and wellness space. In recent months, we’ve noticed a hesitancy to adopt algorithmic-based research techniques. Many traditional life sciences market

and more »


Medical Marketing and Media

Machine Learning Can Decipher Data
Medical Marketing and Media
As researchers and analytics, we interact with a wide array of market research pros in the health and wellness space. In recent months, we've noticed a hesitancy to adopt algorithmic-based research techniques. Many traditional life sciences market ...

and more »

Retooling drug development with next-generation machine learning, Owkin nabs $11M – FierceBiotech

FierceBiotechRetooling drug development with next-generation machine learning, Owkin nabs $11MFierceBiotechUnlike many other AI-based drug development platforms, Owkin Socrates utilizes two machine learning tools—transfer learning and federated learnin…


FierceBiotech

Retooling drug development with next-generation machine learning, Owkin nabs $11M
FierceBiotech
Unlike many other AI-based drug development platforms, Owkin Socrates utilizes two machine learning tools—transfer learning and federated learning. Gilles Wainrib, PhD., co-founder and CSO of Owkin, explained to FierceBiotech via email how they work ...

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