Zscaler Cloud Security | Two-Minute Overview

| March 19, 2018

article image
Zscaler enables the world’s leading organizations to securely transform their networks and applications for a mobile and cloud-first world. Its flagship services, Zscaler Internet Access and Zscaler Private Access, create fast, secure connections between users and applications, regardless of device, location, or network. Zscaler services are 100% cloud delivered and offer the simplicity, enhanced security, and improved user experience that traditional appliances or hybrid solutions are unable to match. Used in more than 185 countries, Zscaler operates a massive, global cloud security platform that protects thousands of enterprises and government agencies from cyberattacks and data loss.

Spotlight

BehavioSec

The company’s Behavioral Biometrics platform is widely deployed across Global 2000 companies for its proven ability to dramatically reduce account fraud and data theft. Founded in 2008 out of groundbreaking academic research, BehavioSec technology allows companies to continuously verify digital identities with superior precision in real-time.

OTHER ARTICLES

AI VERSUS COVID-19 : A SOLDIER WE DID NOT KNOW WE NEED

Article | March 23, 2020

Coronavirus, COVID-19, is the talk of the town over weeks now if not months. The pandemic nightmare continues to terrorize on a global scale. It is bizarre to believe that bustling shopping malls, house full PVR halls, the crowd at Starbucks phase into Mexican Drug Ghost-towns. As of March 23, 2020, more than 349,000 people have contracted the novel coronavirus and at least 15,308 have died, according to a tally by Johns Hopkins University. Although the documented cases in terms of total recovery are at 100,165, the number of causalities by Coronavirus is larger than SARS (2002-20040 and Bird Flu of 2013, and is slowly closing to the total deaths in Swine Flu (2009-2010) i.e. 18,036.

Read More

AI TO READ HUMAN THOUGHTS AND COVERT THEM INTO TEXTS

Article | April 2, 2020

Imagine an age where you can read the thoughts of a person via telepathy like Professor X of X-Men comics and uncover their anarchical plans or a tech that reads the thoughts of a mute person or your pets and helps you have better communication. Well, a team at the University of California, San Francisco, performed this experiment and put us a step closer to the dream. Joseph Makin, co-author of the research team says, “We are not there yet, but we think this could be the basis of a speech prosthesis.” The university developed the AI to decipher up to 250 words in real-time from a set of between 30 and 50 sentences. The university recruited four women participants with a history of epilepsy and already had electrode arrays implanted in their brain to monitor epileptic seizures. These participants were asked to read aloud from 50 set sentences multiple times as the team tracked their neural using electrodes while they were speaking. The sample included “Tina Turner is a pop singer”, “the oasis was a mirage”, “part of the cake was eaten by the dog”, “Those thieves stole 30 jewels”, “how did the man get stuck in the tree” and “the ladder was used to rescue the cat and the man.” The largest group of sentences contained 250 unique words.

Read More

How Google.org accelerates social good with artificial intelligence

Article | March 11, 2020

After realizing the potential to affect change while studying systems engineering at the University of Virginia, Brigitte Hoyer Gosselink began her journey to discover how technology might have a scalable impact on the world. Gosselink worked within international development and later did strategy consulting for nonprofits before joining Google.org, where she is focused on increasing social impact and environmental sustainability work at innovative nonprofits. We talked to her about her efforts as head of product impact to bring emerging technology to organizations that serve humanity and the environment.

Read More

Artificial Intelligence 2020 Stories: The Great, the Glowing and the Gross Truths

Article | January 4, 2021

2020 has been an unprecedented year where we have seen more downs than ups. COVID-19 has impacted every aspect of our lives. But when it comes to digitisation and Artificial Intelligence, we have seen some impactful developments and achievements. As we approach the end of 2020, it is worth to look back at these AI stories to highlight the truths and discuss what it means for AI future direction. The Great Truth: Artificial intelligence played a crucial role in the detection and fight against COVID-19. Indeed, we have seen the emergence of the use of AI at hospitals to evaluate chest CT scans. With the use of deep learning and image recognition, COVID patients were diagnosed thus enabling the medical team to follow the necessary protocols. Another application was the triage of COVID-19. Once a patient has been diagnosed with COVID, AI has been used to predict the likely severity of the illness so the medical staff can prioritize resources and treatments. COVID has highlighted the need to deploy intelligent autonomous agents. As a result, we have seen both robots used at hospitals to diagnose COVID-19 patients and drones deployed to monitor if the public is adhering to social distancing rules. Another major AI contribution in the fight against COVID-19 is in the area of vaccine and drug discovery. Moderna’s vaccine that has been approved by US Food and Drugs Administration has used machine learning to optimise mRNA sequencing. The above is a proof that AI can make great contribution to mankind if it is used for “good”. The Glowing Truths: Some impressive AI results have been achieved. However, to leap forward a holistic and sustainable approach is needed. 2020 has seen some great AI achievements and leaps forward. The first example is Deepmind’s AlphaFold. The model scored highest at the Critical Assessment of Structure Prediction competition. The algorithm takes genetic information as inputs and outputs a three-dimensional structure. The model has impressively addressed a 50-year-old challenge of figuring out want shapes proteins fold into known as the “protein folding problem”. While Deepmind’s AlphaFold is a great achievement, it is noted by some scientists that it is unclear how the model will work with more real-world complex proteins. Thus, more work is needed in this area. The second example is OpenAI’s GPT3. The model is a very large network composed of 96 layers and 175 billion parameters. The model has shown impressive results for several tasks such as NLP questions & answering and generating code. However, it is noted that the model does not have any kind of reasoning and does not understand what it is generating. Furthermore, its large size makes it very expensive. It is also unsustainable carbon footprint wise; its training is equivalent to driving a car to the moon and back. While both AlphaFold and GPT3 models are both impressive achievements, there are some philosophical challenges/ questions that need to be addressed/ answered. The first question is about games/ simulated worlds vs. real world examples. Most often algorithms/models succeed in simulated world but fail in real world as the environment is more complex. How can we close the gap? How can we make the AI models succeed with complex tasks? I guess the first step is to apply AI to a real-world example with varied complexity levels. The second question is about the structure and the size of AI models. Do models have to be big? Can we come up with a new generation of algorithms/ models that are smaller is size and have more efficient computations? Well to answer this question we have to take a pause on deeplearning and explore new venues. The Gross Truths: Ethics and bias remain the main drawbacks of Artificial Intelligence. Over the last year, we had several prominent examples of AI ethics and bias issues. The first example relates to facial recognition: after several calls against mass surveillance, racial profiling and bias, and in light of Black Lives Matter movement starting in the United States, several tech companies such as Microsoft banned the police from using its facial recognition technology. The second example relates to the use of an algorithm to predict exam results during COVID-19 period: after accusations and protests that the controversial algorithm was biased against students from poorer backgrounds, the United Kingdom government was forced to ditch the algorithm. In the absence of regulations and tightened frameworks, ethics and bias will continue to be the main concerns surrounding the use of artificial intelligence. Looking into the future, AI adoption will continue to accelerate, and we will probably see more breakthroughs achieved by only if we start looking at the subject in a holistic and sustainable view. Focusing models on real world problems and reducing the models carbon footprint will be a major step forward. We need to move away from thinking that “more” is always “more”. Sometimes “more” is “less”.

Read More

Spotlight

BehavioSec

The company’s Behavioral Biometrics platform is widely deployed across Global 2000 companies for its proven ability to dramatically reduce account fraud and data theft. Founded in 2008 out of groundbreaking academic research, BehavioSec technology allows companies to continuously verify digital identities with superior precision in real-time.

Events