Il cancro rappresenta il paradigma dell'immortalità. Se nelle cellule sane esiste un meccanismo 'salvifico' di apoptosi, le cellule cancerose non vogliono morire. Mai come adesso ricerca e nuove tecnologie sono in grado di combattere ad armi pari. Quattro esempi. #OneGiantLeap
Cancer-fighting nanorobots programmed to seek and destroy tumors.
Study shows first applications of DNA origami for nanomedicine.
Saturday Keynote at #ODSCWest Carrie Grimes Bostock - Data Science & Automated Decision Making. Automation has inputs, model(s) and potential outputs for action.
What is the goal of your models? Interpretability is important, but not the only thing. Other measures exist, but understanding your model is important to prevent outcomes, override recommendations, and to connect models to other systems - or stacks of models & systems. #ODSCWest
Example: placing racks in the Santa Clara data centre. Considerations, restrictions on space, power, future use of space, etc. Also: People install racks. They need to be able to intervene based on info that the placement model doesn't have. #ODSCWest
Industry starting to confront #ethics in terms of daily applications of #AI algorithms. #Bias based on data - in the case of courts, potentially hundreds of years of biased decisions as part of the training set.
"I'm just an engineer" not an appropriate response. People using #AI products might not understand what or how decisions and predictions are being made, but will be making real world decisions based on them. YOU and your team might be the only ones who do.
First up: @asuonline will be hosting an educational analytics conference in March 2019 focusing on what we can learn from learner data and how to structure learning to better meet learner needs. #ODSCWest
#AI differential - being able to tailor products to specific users == better able to predict value to and from users. E.g. cost to deliver value to user vs. cost to company to deliver that value. Allows ruthless competitive focus - @DataRobot#ODSCWest
2/ As @ZachAbramowitz thinks there's no fun in agreeing: no, I don't think @nwaisb / @KiraSystems & others have benefited (on net) from the AI hype. I'd say hype hurts us all, bc it trades short term gainz to a few players for long-term pain shared by all.
3/ New entrants can feel the need to build #buzz for brand awareness + brand lift. I wouldn't exactly disagree this may help new offerings generate leads + fill their pipeline. Piggybacking on an existing trend with #hype is a shortcut to buzz, BUT with many pitfalls ⚠️
A thread for the "#software is not political" crowd. Whether you like it or not, all software is political because technology AFFECTS PEOPLE. If you came to #tech thinking you'd escape having to think about people, #politics, and society - you were mistaken. I'll show you why...
Before we start, remember this as you read. Just because YOU think the answer to any of these issues is clear-cut, it doesn't mean that issue is not political.
There are people who take the OPPOSITE position with just as much conviction, and they think it's clear-cut too.
Also - as an engineer, a developer or designer - if you choose to ignore the political and societal implications of your #technology, YOU HAVE MADE A POLITICAL CHOICE.
Starting with #MSDyn365#Sales updates: 1. Playbooks: Think of it as Barney Stinson's playbook. Basically set of "automated repeatable sales activities" that help in winning opportunities. Looks like it'll be a set of activities which could be assigned to users
2. #MSDyn365#Sales will feature deeper integration with LinkedIn, including capability to send InMail and adding LinkedIn related step in Business Process Flows
We are currently in the keynote. The speaker is talking about how popular Neo4j is
Keynote now telling us about all the new Neo4j features. Examples: location filter, including 3d. Auto cache reheating. I'm interested to know if auto cache reheating is working with query patterns or meant to replace them or what
Bayes’ Theorem Definitions:
The vertical bar | stands for "given that".
P = Probability.
A & B are events.
P(A) & P(B) are the probabilities of events A and B. Each event is separate from the other.
P(A|B) is the probability of A being true given that event B is true. #SoDS18#ML
Say we have 2 coolers at an owambe: Cooler A is filled with 10packs of small chops only. Cooler B has 5packs of small chops and 5packs of Asun. You are then asked to close your eyes and pick a pack out of one cooler, which pack would you pick? #MachineLearning#SoDS18
Because you know that we have more of small chops in both coolers, your brain is most likely going to tell you have picked a pack of small chops - even when your eyes are closed. This is not wrong. #MachineLearning#SoDS18
Künstliche Intelligenz wird zum Beispiel bei der automatischen #Bilderkennung eingesetzte. Auf thing-translator.appspot.com kann man Gegenstände in die Kamera halten, ein Foto machen und die #KI interpretiert die Bilddaten 📸 🤓
Auch unter cloud.google.com/vision/ können Bilder hochgeladen werden - hier werden sogar noch mehr Informationen angezeigt und einzelne Objekte und Gesichter in den Bildern erkannt (Bildanalyse von Google) 👇🤖 #KI
(1/5) I grew up in the Programming Languages research community and have recently begun attending Machine Learning conferences. One perspective that I don't see much in either community is that #MachineLearning is a form of #programming.
(2/5) PL/formal methods researchers tend to think of programs as engineered objects, and study abstractions/tools for principled engineering. But the big assumption here is that you can formalize your goals and the world in which your programs run. That's not always realistic.
(3/5) In contrast, #MachineLearning lets programs be "found" objects. "I don't have a full spec for my program and can't write the code myself, but here's some data on what it does. Discover it!" This is still #programming, albeit done inductively rather than deductively.
Inspired by the big ol' long list of deep learning models I saw this morning, and @SpaceWhaleRider's love of science-y A-Z lists, I've decided to create an A to Z series of tweets on popular #MachineLearning and #DeepLearning methods / algorithms.
Ready? Here we go:
A is for... the Apriori Algorithm!
Intended to mine frequent itemsets for Boolean association rules (like market basket analysis). Ex: if someone purchases the same products as you, in general, then you'd probably purchase something they've purchased.
This is an ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification+regression. Reduces variance, helps to avoid overfitting.