The 7 Most Disruptive Software Engineering Trends of 2021 (disruptive innovation & 2021 tech trends)

As we begin our journey into 2021, we thought we’d investigate the software engineering trends that are most likely to be at the forefront of innovation in the coming months. To help us do so, we tapped the experience and knowledge of Dr. Jeff Jensen (CEO & Co-Founder of Keto AI) and Martin Do (Software Solutions Architect, and Ex-Microsoft Engineer).

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00:00 – Intro
01:14 – Think-Less and No-Code
02:17 – IoT
03:19 – React & Flutter
04:06 – AI & Machine Learning
06:44 – Cloud & DevOps
08:08 – Remote Work
10:18 – Cyber Security
13:03 – How these trends will impact software engineering
13:55 – How to take advantage of these trends
15:19 – Unpopular Opinions
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Machine Learning for Engineering and Science Applications – Intro Video

Peter Norvig is a Director of Research at Google Inc. Previously he was head of Google’s core search algorithms group, and of NASA Ames’s Computational Sciences Division, making him NASA’s senior computer scientist. He received the NASA Exceptional Achievement Award in 2001. He has taught at the University of Southern California and the University of California at Berkeley, from which he received a Ph.D. in 1986 and the distinguished alumni award in 2006.

This video describes the reasons to become a software engineer!

Credit:

A ride in a Waymo driverless car: https://www.youtube.com/watch?v=2hqTnmn51Fg

Full Self-Driving: https://www.youtube.com/watch?v=tlThdr3O5Qo

ITRI shows OLED Lighting, Chess playing robot “Turk”: https://www.youtube.com/watch?v=jn9ScDKP7dE

Artificial justice: would robots make good judges?: https://www.youtube.com/watch?v=1DJNEUMkNuQ

This video is for non-commercial use.

With the increasing popularity of AI, new frontiers are emerging in predictive maintenance and manufacturing decision science. However, there are many complexities associated with modeling plant assets, training predictive models for them, and deploying these models at scale for near real-time decision support. This talk will discuss these complexities in the context of building an example system.

First, you must have failure data to train a good model, but equipment failures can be expensive to introduce for the sake of building a data set! Instead, physical simulations can be used to create large, synthetic data sets to train a model with a variety of failure conditions.

These systems also involve high-frequency data from many sensors, reporting at different times. The data must be time-aligned to apply calculations, which makes it difficult to design a streaming architecture. These challenges can be addressed through a stream processing framework that incorporates time-windowing and manages out-of-order data with Apache Kafka. The sensor data must then be synchronized for further signal processing before being passed to a machine learning model.

As these architectures and software stacks mature in areas like manufacturing, it is increasingly important to enable engineers and domain experts in this workflow to build and deploy the machine learning models and work with system architects on the system integration. This talk also highlights the benefit of using apps and exposing the functionality through API layers to help make these systems more accessible and extensible across the workflow.

This session will focus on building a system to address these challenges using MATLAB, Simulink. We will start with a physical model of an engineering asset and walk through the process of developing and deploying a machine learning model for that asset as a scalable and reliable cloud service.

More details: https://confengine.com/odsc-india-2019/proposal/10105

Conference Link: https://india.odsc.com

Artificial Intelligence Are we engineering our own obsolescence, Artificial Intelligence Are we engineering our own obsolescence

Artificial intelligence is playing an increasingly important role in new software products, but the workflow of an AI researcher is quite different from the workflow of the software developer. Peter Norvig explains how the two can come together.

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Silicon Valley Deep Learning Group is honored to host Peter Norvig. Peter talks about Deep Learning and Understandability versus Software Engineering and Verification.

Peter Norvig is a Director of Research at Google Inc. Previously he was head of Google’s core search algorithms group, and of NASA Ames’s Computational Sciences Division, making him NASA’s senior computer scientist. He received the NASA Exceptional Achievement Award in 2001. He has taught at the University of Southern California and the University of California at Berkeley, from which he received a Ph.D. in 1986 and the distinguished alumni award in 2006. He was co-teacher of an Artifical Intelligence class that signed up 160,000 students, helping to kick off the current round of massive open online classes. His publications include the books Artificial Intelligence: A Modern Approach (the leading textbook in the field), Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX. He is also the author of the Gettysburg Powerpoint Presentation and the world’s longest palindromic sentence. He is a fellow of the AAAI, ACM, California Academy of Science and American Academy of Arts & Sciences.

See Peter’s web site for more info:

http://norvig.com/bio.html

Artificial Intelligence Are we engineering our own obsolescence, Artificial Intelligence Are we engineering our own obsolescence

Filmed at Shoreditch Town Hall on 11th June 2015

Featuring: Nick Bostrom, Daniel Glaser, Murray Shanahan, Riva-Melissa Tez, Adam Rutherford (chair).

Some of the smartest minds have sounded the alarm recently about artificial intelligence. Physicist Stephen Hawking has said that it could spell the end of the human race. Tech pioneer Elon Musk has warned that with AI ‘we are summoning the demon’. Given that the world is already full of the smart precursors of artificial intelligence – Siri, Google Translate, driverless cars etc, why the warning bells? Because many experts believe that it is only a matter of decades before we create a form of AI that is more than just a useful tool and becomes an autonomous, self-aware entity which could take off on its own, self-replicating and redesigning itself and ultimately wiping out the human race.

Not everyone is so pessimistic, however. Many experts believe that superintelligent machines, far from being a threat, will be our allies. There will be major breakthroughs in science and health, where specialists working alongside computers do better than either a human or a machine on their own. Others, most notably Google futurist Ray Kurzweil, are looking forward to a time when superintelligence will greatly enhance human capacities by means of computer implants that improve the brain and the body. Ultimately we could merge with our superhuman creations, defying ageing and death.

Elon Musk and other panellists talk AI and answer the question: “If we succeed in building human-level Artificial general intelligence (AGI), then what are the .

Ray Kurzweil explores how and when we might create human-level artificial intelligence at the January 2017 Asilomar conference organized by the Future of .

Filmed at Shoreditch Town Hall on 11th June 2015 Featuring: Nick Bostrom, Daniel Glaser, Murray Shanahan, Riva-Melissa Tez, Adam Rutherford (chair).

Artificial intelligence is getting smarter by leaps and bounds — within this century, research suggests, a computer AI could be as smart as a human being.

Want to join the debate? Check out the Intelligence Squared website to hear about future live events and podcasts: http://www.intelligencesquared.com
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Filmed at Shoreditch Town Hall on 11th June 2015

Featuring: Nick Bostrom, Daniel Glaser, Murray Shanahan, Riva-Melissa Tez, Adam Rutherford (chair).

Some of the smartest minds have sounded the alarm recently about artificial intelligence. Physicist Stephen Hawking has said that it could spell the end of the human race. Tech pioneer Elon Musk has warned that with AI ‘we are summoning the demon’. Given that the world is already full of the smart precursors of artificial intelligence – Siri, Google Translate, driverless cars etc, why the warning bells? Because many experts believe that it is only a matter of decades before we create a form of AI that is more than just a useful tool and becomes an autonomous, self-aware entity which could take off on its own, self-replicating and redesigning itself and ultimately wiping out the human race.

Not everyone is so pessimistic, however. Many experts believe that superintelligent machines, far from being a threat, will be our allies. There will be major breakthroughs in science and health, where specialists working alongside computers do better than either a human or a machine on their own. Others, most notably Google futurist Ray Kurzweil, are looking forward to a time when superintelligence will greatly enhance human capacities by means of computer implants that improve the brain and the body. Ultimately we could merge with our superhuman creations, defying ageing and death.

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