Is manual farming sustainable as the need for agricultural products grow in demand? Can technology help? How does it impact lives of farmers? Is it the right thing to do? Like any other applications of technology, there are more questions than answers. The following links are just a set of leading indicators of trends.
Agricultural vehicles known as “cucumber flyers” enable as many as 50 seasonal workers to harvest crops.
Experts from Fraunhofer IPK in Berlin, along with other German and Spanish researchers, are studying the potential for automating cucumber harvests in the scope of the EU project CATCH, which stands for “Cucumber Gathering – Green Field Experiments.” Project partners are the Leibniz Institute for Agricultural Engineering and Bioeconomy in Germany and the CSIC-UPM Centre for Automation and Robotics (CAR) in Spain.
I was talking to a group of faculty members at KCG Tech on why we should ask schools to host An Hour of Code.
The Hour of Code started as a one-hour introduction to computer science, designed to demystify “code”, to show that anybody can learn the basics, and to broaden participation in the field of computer science. It has since become a worldwide effort to celebrate computer science, starting with 1-hour coding activities but expanding to all sorts of community efforts.
Here are some reasons why you should be interested in hosting an hour of code or help schools to host it.
- This grassroots campaign is supported by over 400 partners and 200,000 educators worldwide.
- It is an international movement to get people interested in learning to code.
- The first step in teaching programming is to get the learner engaged. Next steps include creating curiosity and giving them a sense of wonder. Show them what they can do with the code in a few minutes.
- Students will do something different and have a lot of fun while learning. In the past couple of instances where we conducted an hour of code, many 7th graders went beyond the hour, refusing to leave the computer lab.
- The program will be run mostly by student volunteers and techies. We are trying to get students involved in social causes. We believe the best form for students to learn, is by teaching.
There are several cool tools you can use for thinking. Two of my favorite ones are Mindmaps and Lists.
List of 100 is a great way to really stretch your mind. Here is how you do it. Take a problem or idea. Create a list of 100 things that come to your mind. In the case of a problem, it may be a hundred ways to solve it. In the case of an idea it may be a list of hundred thoughts (typically questions related to – Why, What, Who, When, How, Where).
I first came across the List of 100 here. Since then, I have created lists of 100 individually and in groups. We had great fun doing it and learned a lot. List of 100 is both a thinking tool and a group collaboration tool. Give it a try.
I really enjoyed watching “The Culture of Innovation” from MIT Technology Review.
The talk covers several interesting topics worth exploring.
Permission less innovation and Innovation at the edges
A culture of practice over theory
The concept of Social Investing
Connectivity in Communities
Peripheral vision and Pattern Recognition and how they are the total opposite of focus and execution
Cultures and sub-cultures
My favorite quote from the talk:
We so cherish focus, execution and they are the opposites of peripheral vision, pattern recognition
Peripheral vision and pattern recognition lead to discovering new ways of doing things.
I was talking to a student. He is fascinated with a robot that cleans pipes. He had a prototype and won some awards. He wanted to discuss it.
We sat with him and brainstormed many ideas for the design at a very high level. I encouraged him to think about a different cleaner robot – one that cleans water tanks. Our discussion lasted half an hour and it was one of the most rewarding exercises I did today.
Thinking through the design of products is fun. When you do it as a small passionate group, it is even more fun. One of the reasons I hang out with a lot of engineering students.
Most of my school and college life was spent in learning lots of facts. I also learned principles and concepts but not in any coherent manner. I was not sure why I was learning, what I was learning. Our teachers (if they knew), forgot to tell us the “Whys?”. Some of this learning was fun and enjoyable and reasonably effortless but some of it was not.
When I started working, I started learning by doing. This was way more fun since I had a context on why I had to learn certain things. I retained my knowledge better since Iusing it. When you learn by doing or learn so that you can use it, the style is very different. You learn on demand and if some of what you are learning does not make sense, you dig deeper and try to find out why something works the way it does. I will call this as exploratory learning and it certainly is a lot more effective.
I think people will learn better, if:
- They know why they are learning (learning by understanding the larger context)
- They are allowed to explore (learning by exploring and discovering)
- They are challenged by tasks that require learning (learning by doing)
- They have the freedom to learn in their own ways (Seven freedoms of Learning)
- We make learning as interesting as playing games
When I give talks on Machine Learning, I often get these questions:
- What is Machine Learning?
- What are some Machine Learning Applications?
- Is Machine Learning Mature?
- Who is using Machine Learning?
- How do we get started?
If you are using Google or Bing Search, if you get recommendations for books or other products from Amazon, if you are getting hints for the next word to type on a mobile keyboard, you are already using Machine Learning.
Here is a sample list of Machine Learning applications.
From Apple’s Core ML Brings AI to the Masses:
- Real Time Image Recognition
- Sentiment Analysis
- Search Ranking
- Speaker Identification
- Text Prediction
- Handwriting Recognition
- Machine Translation
- Face Detection
- Music Tagging
- Entity Recognition
- Style Transfer
- Image Captioning
- Emotion Detection
- Text Summarization
From Seven Machine Learning Applications at Google
- Google Translate
- Google Voice Search
- Gmail Inbox Smart Reply
- Google Photos
- Google Cloud Vision API
Also, see – How Google is Remaking Itself as a “Machine Learning First” Company.
While Apple, Google, Facebook, Amazon, IBM, and Microsoft are the most visible companies in the AI space, take a look at business applications of Machine Learning.
What is Machine Learning? It is a common question that I get asked a lot. I wanted to find a simple, intuitive definition. After doing a few Google searches, I settled on this one from Arthur Samuel.
from Arthur Samuel (in 1959)
“[Machine Learning is the] field of study that gives computers the ability to learn without being explicitly programmed.”
It is a field of study. I like that. I picked this after Googling and finding over 100 descriptions. Here is a shorter curated list of results from this Google Search. From this list, you may find that Machine Learning is:
- A technique
- A field of study
- An application
- A Method
- A type of AI
- A sub-field of AI
- A general term
- A cure-all for all human problems (just kidding)
- A data based application generator
- A statistical method of learning from data
- A mapping function of inputs to outputs
So, what do you think is Machine Learning?