We haven’t even discussed the abilities to detect your walking patterns (already being used by some police agencies), monitor scents, track microbial cells or identify you from your body shape. More and more organizations are looking for contactless methods to authenticate, especially relevant today. What all these biometrics technologies have in common is that they are using some combination of physiological and behavioral methods to make sure you are you. There are certain things people just can’t fake. You can’t fake a heartbeat, which is as unique as a retinal scan or fingerprint. You can’t easily fake how you walk. Even your typing and writing styles give off a distinct and unique signature. ... Some of the best innovators are threat actors. They may not be able to replicate your heartbeat today, but what about tomorrow? The not-too-distant future could include a “Mission: Impossible“ scenario with 3D printers that generate a ‘body suit’ (think wetsuit) that can have a simulated heartbeat uploaded into it. This all may sound like science fiction right now, but not too long ago, would it have not been silly to think that your heartbeat could be identified through clothes using a laser from over 200 yards away?
Though technical skills, like those accompanying cyber security and emerging tech are a focus, IT professionals are coming to realise that non-technical skills are a critical element of their career development and IT management. When asked which of these were most important, IT pros listed project management (69%), interpersonal communication (57%), and people management (53%). According to the LinkedIn 2020 Emerging Jobs Report, the demand for soft skills like communication, collaboration, and creativity will continue to rise across the SaaS industry. Despite the budget and skills issues IT professionals report, 53% of those surveyed said they’re comfortable communicating with business leadership when requesting technology purchases, investing time/budget into team trainings, and the like. Though developing tech skills is often informed by current areas of expertise, the 2020 IT Trends Report reveals strong IT performance is about more than IT skills. Interpersonal skills are commonly referred to as “soft skills”, which is misleading. They rank highly in overall importance, meaning soft skills aren’t optional. They’re human skills — everyone needs to relate to other people and speak in a way they can understand. My advice in this area would be to find a mentor, someone on your team who can help you learn. Practice your communication skills and try your hand at new specialties like project management.
Fast forward to today. Within the information governance space, there are two terms that have been used quite frequently in recent years: analytics and AI. Often they are used interchangeably and are practically synonymous. Organizations—as well as the software vendors that supply their needs—have largely tapped analytics to provide deeper information beyond basic indexed searching, which typically involves applying Boolean logic to keywords, date ranges, and data types. Search concepts have expanded to filter out application-specific metadata (e.g., parsing mail distribution lists, application login time, login/logout/idle times in chat and collaborative rooms, etc.). Today's search also includes advanced capabilities such as stemming and lemmatization—methods for matching queries with different forms of words—and proximity search, allowing searchers to find the elusive needle in the haystack. The latest whiz-bang features that are all the buzz within the information governance space are analytics (or predictive analytics) and AI (or artificial intelligence/machine learning). These are here to stay, and we are just beginning to scratch the surface of their many uses.
New machine-learning models are measured against large, curated data sets that lack noise and have well-defined, explicitly labeled categories (cat, dog, bird). Deep learning does well for these problems because it assumes a largely stable world (pdf). But in the real world, these categories are constantly changing over time or according to geographic and cultural context. Unfortunately, the response has not been to develop new methods that address the difficulties of real-world data; rather, there’s been a push for applications researchers to create their own benchmark data sets. The goal of these efforts is essentially to squeeze real-world problems into the paradigm that other machine-learning researchers use to measure performance. But the domain-specific data sets are likely to be no better than existing versions at representing real-world scenarios. The results could do more harm than good. People who might have been helped by these researchers’ work will become disillusioned by technologies that perform poorly when it matters most. Because of the field’s misguided priorities, people who are trying to solve the world’s biggest challenges are not benefiting as much as they could from AI’s very real promise.
One may ask what is the difference between ANN and DL. The name Artificial Neural Network is inspired from a rough comparison of it’s architecture with human brain. Although some of the central concepts in ANNs were developed in part by drawing inspiration from our understanding of the brain, ANN models are not models of the brain. In reality, there is no great similarity between an ANN and it’s method of operation with human brain, neurons, synapses and it’s modus operandi. However the fact that the ANN is a consolidation of one or more layers of neurons, that help in solving perceptual problems - which is based human intuition, the name goes well. ANN essentially is a structure consisting of multiple layers of processing units (i.e. neurons) that take input data and process it through successive layers to derive meaningful representations. The word deep in Deep Learning stands for this idea of successive layers of representation. How many layers contribute to a model of the data is called the depth of the model. Below diagram illustrates the structure better as we have a simple ANN with only one hidden layer and a DL Neural Network (DNN) with multiple hidden layers.
The Department of Homeland Security on June 29 issued an alert about "BlueLeaks" hacking of Nesential, saying a criminal hacker group called Distributed Denial of Secrets - also known as "DDS" and "DDoSecrets" - on June 19 "conducted a hack-and-leak operation targeting federal, state, and local law enforcement databases, probably in support of or in response to nationwide protests stemming from the death of George Floyd." The hacking group leaked 10 years of data from 200 police departments, fusion centers and other law enforcement training and support resources around the globe, the DHS alert noted. The 269 GB data dump was posted on June 19 to DDoSecrets' site, the hacking group said in a tweet that has since been removed. The data came from a wide variety of law enforcement sources and included personally identifiable information and data concerning ongoing cases, DDoSecrets claimed in a tweet. Several days after DDoSecrets revealed the law enforcement information through its Twitter account in June, the social media platform permanently removed the DDoSecrets account, citing Twitter rules concerning posting stolen data.
At the top of this list, we have the Remote Desktop Protocol (RDP). Reports from Coveware, Emsisoft, and Recorded Future clearly put RDP as the most popular intrusion vector and the source of most ransomware incidents in 2020. "Today, RDP is regarded as the single biggest attack vector for ransomware," cyber-security firm Emsisoft said last month, as part of a guide on securing RDP endpoints against ransomware gangs. Statistics from Coveware, a company that provides ransomware incident response and ransom negotiation services, also sustain this assessment; with the company firmly ranking RDP as the most popular entry point for the ransomware incidents it investigated this year. ... RDP has been the top intrusion vector for ransomware gangs since last year when ransomware gangs have stopped targeting home consumers and moved en-masse towards targeting companies instead. RDP is today's top technology for connecting to remote systems and there are millions of computers with RDP ports exposed online, which makes RDP a huge attack vector to all sorts of cyber-criminals, not just ransomware gangs.
“When looking at the ecosystem of election security, political campaigns can be soft targets for cyberattacks due to the inability to dedicate resources to sophisticated cybersecurity protections,” Woolbright said. “Campaigns are typically short-term, cash strapped operations that do not have an IT staff or budget necessary to promote long-term security strategies.” For state and local governments, constituents are accessing online information about voting processes and polling stations in noticeably larger numbers of late – Cloudflare said that it has seen increases in traffic ranging from two to three times the normal volume of requests since April. So perhaps it’s no coincidence that the firm found that government election-related sites are experiencing more attempts to exploit security vulnerabilities, with 122,475 such threats coming in per day (including an average of 199 SQL injection attempts per day bent on harvesting information from site visitors). “We believe there are a wide range of factors for traffic spikes including, but not limited to, states expanding vote-by-mail initiatives and voter registration deadlines due to emergency orders by 53 states and territories throughout the United States,” Woolbright said.
"We were focused on the idea that autonomous was the same as intelligence," said Angle. "We were told that wasn't intelligent and customers wanted collaboration." The COVID-19 pandemic pushed the collaboration theme with customers and robots because there was no choice. People are home more than ever so more cleaning coordination is needed. Meanwhile, iRobot found customers were home more yet had less time to clean. More time at home also meant more messes. Indeed, iRobot has seen strong demand during the COVID-19 pandemic. The company saw premium robot sales jump 43% in the second quarter with strong performance across its international business. Roomba i7 Series, s9 Series, and Braava jet m6 also performed well. For the second quarter, iRobot delivered revenue of $279.9 million, up 8% from a year ago. First-half revenue for 2020 was $472.4 million. iRobot reported second quarter earnings of $2.07 a share. Julie Zeiler, CFO of iRobot, said that Roomba was 90% of the product mix in the second quarter and the company's e-commerce business performed well.
Using a simple three-step process - setup, predict and learn - it can be thought of as machine learning from scratch. The system starts off with a selection of 100 algorithms made by randomly combining simple mathematical operations. A sophisticated trial-and-error process then identifies the best performers, which are retained - with some tweaks - for another round of trials. In other words, the neural network is mutating as it goes. When new code is produced, it's tested on AI tasks - like spotting the difference between a picture of a truck and a picture of a dog - and the best-performing algorithms are then kept for future iteration. Like survival of the fittest. And it's fast too: the researchers reckon up to 10,000 possible algorithms can be searched through per second per processor (the more computer processors available for the task, the quicker it can work). Eventually, this should see artificial intelligence systems become more widely used, and easier to access for programmers with no AI expertise. It might even help us eradicate human bias from AI, because humans are barely involved. Work to improve AutoML-Zero continues, with the hope that it'll eventually be able to spit out algorithms that mere human programmers would never have thought of.
Quote for the day:
"Luck is what happens when preparation meets opportunity." -- Darrell Royal