Roskomnadzor has also exerted pressure on Google to remove certain sites on Russian searches.
Director of National Intelligence Dan Coats told Congress last month that Russia, as well as other foreign actors, will increasingly use cyber operations to “threaten both minds and machines in an expanding number of ways—to steal information, to influence our citizens, or to disrupt critical infrastructure.”
Remember that a blockchain is an immutable, sequential chain of records called Blocks. They can contain transactions, files or any data you like, really. But the important thing is that they’re chained together using hashes.
“Formulating a product, you better know about ethics and understand legal frameworks.”
These days a growing number of people are concerned with bringing more talk of ethics into technology. One question is whether that will bring change to data-science curricula.
Following major data breaches and privacy scandals at tech companies like Facebook, universities including Stanford, the University of Texas and Harvard have all added ethics courses into computer science degree programs to address tech’s “ethical dark side,” the New York Times has reported.
As more college and universities consider incorporating humanities courses into technical degree programs, some are asking what kind of ethics should be taught.
Both jazz and classical art forms require not only music literacy, but for the musician to be at the top of their game in technical proficiency, tonal quality and creativity in the case of the jazz idiom. Jazz masters like John Coltrane would practice six to nine hours a day, often cutting his practice only because his inner lower lip would be bleeding from the friction caused by his mouth piece against his gums and teeth. His ability to compose and create new styles and directions for jazz was legendary. With few exceptions such as Wes Montgomery or Chet Baker, if you couldn’t read music, you couldn’t play jazz.
Besides the decline of music literacy and participation, there has also been a decline in the quality of music which has been proven scientifically by Joan Serra, a postdoctoral scholar at the Artificial Intelligence Research Institute of the Spanish National Research Council in Barcelona. Joan and his colleagues looked at 500,000 pieces of music between 1955-2010, running songs through a complex set of algorithms examining three aspects of those songs:
1. Timbre- sound color, texture and tone quality
2. Pitch- harmonic content of the piece, including its chords, melody, and tonal arrangements
3. Loudness- volume variance adding richness and depth
In an interview, Billy Joel was asked what has made him a standout. He responded his ability to read and compose music made him unique in the music industry, which as he explained, was troubling for the industry when being musically literate makes you stand out. An astonishing amount of today’s popular music is written by two people: Lukasz Gottwald of the United States and Max Martin from Sweden, who are both responsible for dozens of songs in the top 100 charts. You can credit Max and Dr. Luke for most the hits of these stars:
Katy Perry, Britney Spears, Kelly Clarkson, Taylor Swift, Jessie J., KE$HA, Miley Cyrus, Avril Lavigne, Maroon 5, Taio Cruz, Ellie Goulding, NSYNC, Backstreet Boys, Ariana Grande, Justin Timberlake, Nick Minaj, Celine Dion, Bon Jovi, Usher, Adam Lambert, Justin Bieber, Domino, Pink, Pitbull, One Direction, Flo Rida, Paris Hilton, The Veronicas, R. Kelly, Zebrahead
Way back in 1983, I identified A.I. as one of 20 exponential technologies that would increasingly drive economic growth for decades to come.
Artificial intelligence applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, decision trees and machine learning to recognize patterns from vast amounts of data, provide insights, predict outcomes and make complex decisions. A.I. can be applied to pattern recognition, object classification, language translation, data translation, logistical modeling and predictive modeling, to name a few. It’s important to understand that all A.I. relies on vast amounts of quality data and advanced analytics technology. The quality of the data used will determine the reliability of the A.I. output.
Machine learning is a subset of A.I. that utilizes advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon’s Alexa, Apple’s Siri, or any of the others from companies like Google and Microsoft all get better every year thanks to all of the use we give them and the machine learning that takes place in the background.
Deep learning is a subset of machine learning that uses advanced algorithms to enable an A.I. system to train itself to perform tasks by exposing multi-layered neural networks to vast amounts of data, then using what has been learned to recognize new patterns contained in the data. Learning can be Human Supervised Learning, Unsupervised Learningand/or Reinforcement Learning like Google used with DeepMind to learn how to beat humans at the complex game Go. Reinforcement learning will drive some of the biggest breakthroughs.
Autonomous computing uses advanced A.I. tools such as deep learning to enable systems to be self-governing and capable of acting according to situational data without human command. A.I. autonomy includes perception, high-speed analytics, machine-to-machine communications and movement. For example, autonomous vehicles use all of these in real time to successfully pilot a vehicle without a human driver.
Augmented thinking: Over the next five years and beyond, A.I. will become increasingly embedded at the chip level into objects, processes, products and services, and humans will augment their personal problem-solving and decision-making abilities with the insights A.I. provides to get to a better answer faster.
Technology is not good or evil, it is how we as humans apply it. Since we can’t stop the increasing power of A.I., I want us to direct its future, putting it to the best possible use for humans.
1. Portfolio assessment is not new to education.
Digital portfolios came into prominence in the 1990s, around the time when computers became commonplace in classrooms. David Niguidula, a pioneer in this alternative form of assessment, coined the term “digital student portfolios.” He defines them as “an online collection of student work for a particular purpose and audience.” Digital portfolios cut the distance between student thinking and evidence of learning. There is no longer a need to represent understanding through a score or a grade.
2. . The best digital portfolios are process oriented.
A myth in education is that we should only showcase student’s best artifacts of learning. We might think of an artist’s body of work when considering digital portfolios as an alternative assessment.
3. It’s not a digital portfolio unless students are in charge.
4. Digital student portfolios are about more than just assessment.
The best digital portfolio processes do more than serve as an evaluation tool. They help the student develop a stronger sense of themselves as a learner and see their growth over time, such as through a series of drafts posted toward a final project and presentation.