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Artificial intelligence for construction safety, 3D printing part of new technologies trialled by HDB

It added: “The automated system will reduce the risk of inconsistent safety standards across different supervising personnel, as well as human errors that could occur due to fatigue.” Trials have been carried out at the Clementi Peaks Build-to-Order (BTO) site since September, and are expected to end by the fourth quarter of 2020.

If the trials are successful, HDB said it could potentially scale up the tool to cover other areas to enhance worksite safety, such as workers standing in the way of vehicle pathways, site traffic management and workers entering confined spaces without permission.

3D CONCRETE PRINTING HDB said the AI system is one example of how it has stepped up its investment in technology to uncover newer, better ways of working, amid the pressures and challenges in manpower and resources faced by the built environment sector.  One other way it is doing so is exploring the use of 3D concrete printing technology to expand its design capabilities.  The process removes the need for moulds or formworks, allowing objects with “intricate detail or geometric forms that would be near impossible to create with traditional methods”, said HDB. 

In anticipation of residents’ concerns, HDB said residents will be notified in advance, the area below flights paths will be cordoned off for safety and that “any images of residents captured during (the) scanning process would be masked out, before the captured data is used for analysis”.

François Chollet: Keras, Deep Learning, and the Progress of AI | Artificial Intelligence Podcast

This conversation is part of the Artificial Intelligence podcast.INFO:Podcast website: episodes playlist: playlist: LINKS:François twitter:çois web: - Introduction1:14 - Self-improving AGI7:51 - What is intelligence?15:23 - Science progress26:57 - Fear of existential threats of AI28:11 - Surprised by deep learning30:38 - Keras and TensorFlow 2.042:28 - Software engineering on a large team46:23 - Future of TensorFlow and Keras47:53 - Current limits of deep learning58:05 - Program synthesis1:00:36 - Data and hand-crafting of architectures1:08:37 - Concerns about short-term threats in AI1:24:21 - Concerns about long-term existential threats from AI1:29:11 - Feeling about creating AGI1:33:49 - Does human-level intelligence need a body?1:34:19 - Good test for intelligence1:50:30 - AI winterCONNECT:- Subscribe to this YouTube channel- Twitter: LinkedIn: Facebook: Instagram: Medium: Support on Patreon:

How Artificial Intelligence Is Changing Cyber Security Landscape and Preventing Cyber Attacks

AI can be used to disguise attacks so effectively that one might never know that their network or device has been affected.  So, the three main implications of Artificial Intelligence to the threat landscape are the augmentation of today’s threats and attacks, the development of new threats, and the variation of the nature of existing threats.

It is also playing a significant role in the ongoing fight against cybercrime.  Following are some of the ways Artificial Intelligence (AI) and Machine Learning (ML) are making a difference by giving the much-needed boost to cybersecurity.  Related:  Eliminating Cyber Threats in 2020: Why Enterprises Need to Rethink Cyber security Organizations have to be able to detect a cyber-attack in advance to be able to thwart whatever the adversaries are attempting to achieve.

Machine learning is that part of Artificial Intelligence which has proven to be extremely useful when it comes to detecting cyber threats based on analyzing data and identifying a threat before it exploits a vulnerability in your information systems.  Machine Learning enables computers to use and adapt algorithms based on the data received, learning from it, and understanding the consequent improvements required.

In a cybersecurity context, this will mean that machine learning is enabling the computer to predict threats and observe any anomalies with a lot more accuracy than any human can.  Traditional technology relies too much on past data and cannot improvise in the way that AI can.

Let’s face reality, most of us are quite lazy with our passwords – often using the same one across multiple accounts, relying on the same password since ages, keeping account of them neatly as a draft message in our device, etc.

Fortunately, AI-ML may play a significant role in preventing and deterring phishing attacks.  AI-ML can detect and track more than 10,000 active phishing sources and react and remediate much quicker than humans can.

Instead, these AI-based systems proactively look for potential vulnerabilities in organizational information systems, and they do so by effectively combining multiple factors, such as hackers’ discussions on the dark web, reputation of the hacker, patterns used, etc.

That does not only save time but also a lot of effort and resources which we can instead apply to areas of technological development and advancement.  6.  Behavioral Analytics with AI Another promising enhancement of security by AI comes from its behavioral analytics ability.

The details can include everything from your typical login times and IP addresses to your typing and scrolling patterns.  If at any time, the AI algorithms notice unusual activities or any behavior that falls outside your standard patterns, it can flag it as being done by a suspicious user or even block the user.

The activities that tick off the AI algorithms can be anything from large online purchases shipped to addresses other than yours, a sudden spike in document download from your archived folders, or a sudden change in your typing speed.

In the wrong hands, it can do exponential damage and become an even stronger threat to cybersecurity.  As technology evolves, the adversaries are also enhancing their attack methods, tools, and techniques to exploit individuals and organizations.

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