Haha! This grandma didn’t know that behind Google were some powerful algorithms that try to predict the best answer to your request. So she say “please” and”thanks”. Yet, this is really what AI is doing, it is rendering us services, and we shall be more grateful for this.

In the world where AI Assistant is designed to be the perfect 24/7 employee, maybe we could add the rule that the politeness matters; like this grandma : )

https://www.theguardian.com/uk-news/2016/jun/16/grandmother-nan-google-praises-search-thank-you-manners-polite

Here are two articles that I discovered today:

77% percent of the time spent by a data scientist is related to non-machine learning algorithms selection, testing and refinement.

1. Data Processing
2. Training Sets creation
3. Machine Learning Algorithms testing, evaluation and selection
4. Deployment and A/B testing

http://fgiasson.com/blog/index.php/2017/03/10/a-machine-learning-workflow/

An really comprehensible tour of datascience implementation at Stichfix.

“Our business model enables unprecedented data science, not only in recommendation systems, but also in human computation, resource management, inventory management, algorithmic fashion design and many other areas. Experimentation and algorithm development is deeply engrained in everything that Stitch Fix does. We’ll describe a few examples in detail as you scroll along.”

http://algorithms-tour.stitchfix.com/#state-machines

This article lists some very interesting first attempts to make “automatons” ancestors of robots. Far before informatics existed, our ancestors were using disks, gears, etc to make those extremely cute “automatons”moving with amazing grace on very few movements. It is amazing to think that humans have been fascinated by imitating nature with no other purpose that to better understand what is hidden behind the body.

Nowadays, you can still buy an automaton bird made with finely cut pieces where the beauty is the mechanism itself and the almost perfect imitation of the natural grace of the epitome of grace: the bird.

https://www.bbvaopenmind.com/en/automatons-the-ancestors-of-the-current-robots/?utm_source=facebook&utm_medium=techreview&utm_campaign=MITcompany&utm_content=Automatons

This link shows the 8 must have excel templates that all PMs should have.

• 1 page status report
• Project and task tracker
• Risk Register
• Issue Tracker
• Project Budget Tracker
• Timesheet Tracker
• PM Dashboard

https://www.projectmanager.com/blog/8-must-pm-excel-templates

You get what was announced at Cisco’s (NASDAQ: CSCO) annual IT and communications conference, Cisco Live. It’s an integration between Redbooth, a project management platform, and Cisco Spark, a cloud-based messaging platform, that utilizes AI-inspired natural language processing to communicate.

Think of it as Apple’s Siri applied to business. Instead of asking about the weather, users ask about various aspects of their project portfolio and team’s status in their own words. Rather than talking to the interface, however, users type their questions:

https://smallbiztrends.com/2016/07/project-management-assistant-ai.html

I was quite surprise to not hear much about Microsoft about AI applied to Project Management. Here are some more details about how Microsoft is applying his AI vision to Project Management.

http://mandmglobal.com/microsoft-launches-ai-tool-to-carry-out-project-management-tasks/

And here the vision of the final vision of Microsoft

https://www.youtube.com/watch?v=xRlC1f1baCc&t=2s

Does AI have a PR issue? Surprisingly some very weel-known academicians think so: Jean Ponce, Yann LeCun and now this wonderful article from Jerry Kaplan:

https://www.technologyreview.com/s/603761/ais-pr-problem/

Some highlights:

• Had artificial intelligence been named something less spooky, we’d probably worry about it less.

• While it’s true that today’s machines can credibly perform many tasks (playing chess, driving cars) that were once reserved for humans, that doesn’t mean that the machines are growing more intelligent and ambitious. It just means they’re doing what we built them to do.

• AI makes use of some powerful technologies, but they don’t fit together as well as you might expect.

• But neither approach is the Holy Grail of intelligence. Indeed, they coexist rather awkwardly under the label of artificial intelligence. The mere existence of two major approaches with different strengths calls into question whether either of them could serve as a basis for a universal theory of intelligence.

• Instead, the accomplishments so breathlessly reported are often cobbled together from a grab bag of disparate tools and techniques.

• Perhaps a less provocative description would be something like “anthropic computing.”

• We should stop describing these modern marvels as proto-humans and instead talk about them as a new generation of flexible and powerful machines.

• Rather, we should resist our predisposition to attribute human traits to our creations and accept these remarkable inventions for what they really are—potent tools that promise a more prosperous and comfortable future.

Meanwhile some very creative images of an AI brain:

Further photos here: https://www.graphcore.ai/blog/what-does-machine-learning-look-like?utm_medium=social&utm_source=linkedin