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    8Nov, 2023
    The 10 most in-demand hard skills for 2023

    Technical roles are some of the most competitive for employers to fill, so it’s not surprising that many of the most in-demand hard skills align with these roles.

    The most in-demand hard skills this year are:

    1. Software development

    The COVID-19 pandemic accelerated the digital transformation and software development skills are needed to support this shift. Several popular programming languages, including Python, Java, and JavaScript, are also among the most in-demand hard skills for 2023.

    Reskill/upskill: Programming Foundations: Fundamentals with Annyce Davis. Get certified via this learning path: Career Essentials in Software Development

    2. SQL

    Many companies manage and analyze large, complex data sets in order to perform key business functions. SQL is an essential skill to retrieve and understand that data so it can be used to make informed business decisions.

    Reskill/upskill: SQL Essential Training with Walter Shields

    3. Finance

    Finance skills have always been a core need for most businesses, though looming economic uncertainty makes it more important for companies to get their finances in order. Careful planning can help companies forecast for various scenarios so they can adapt to unpredictable market conditions — whether that means an economic contraction or expansion.

    Reskill/upskill: Reading Corporate Financial Statements with Denise Probert

    4. Python

    Python is one of the most used programming languages today — and it will likely remain so for the foreseeable future. Python’s compatibility with all major operating systems and wide range of use cases makes it a worthwhile skill for technical workers to master.

    Reskill/upskill: Python Essential Training with Ryan Mitchell

    5. Java

    Java is another popular open-source programming language. Like Python, it’s platform-independent and has a broad range of applications. It’s most useful for designing web and phone apps, as well as cloud computing, making it hyper-relevant in our increasingly digital world.

    Reskill/upskill: Hands-On Introduction: Java with Angie Jones

    6. Data analysis

    Data is collected in every business function and must be analyzed to gain insights that can be used to make informed decisions. Effective data analytics can help organizations optimize performance and reduce costs.

    Reskill/upskill: Learning Data Analytics: 1 Foundations with Robin Hunt. Get certified with this learning path: Career Essentials in Data Analysis

    7. JavaScript

    JavaScript is a scripting language that’s used to develop dynamic web pages and platforms that automatically update with the latest information and multimedia required. As the most commonly used programming language, JavaScript is already familiar to many developers.

    Reskill/upskill: JavaScript Essential Training with Morten Rand-Hendriksen

    8. Cloud computing

    Cloud computing has largely displaced on-premise software due to increased accessibility, scalability, and cost savings. Engineers with cloud computing skills are still in very high demand as companies build and maintain these solutions. 

    9. Operations

    Operations skills help organizations manage their resources and processes so they run more efficiently. This can result in improved productivity, lower costs, and a stronger competitive advantage.

    Reskill/upskill: Quality Management for Operational Excellence with Richard Chua

    10. Customer relationship management (CRM)

    It’s easier — and less expensive — to retain an existing customer than it is to find a new one. Customer relationship management skills enable workers to build strong relationships with their current and prospective customers so they can increase loyalty and repeat business.

    Resource : https://www.linkedin.com/business/talent/blog/talent-strategy/linkedin-most-in-demand-hard-and-soft-skills

    7Nov, 2023
    NASA Finds Record-Breaking Black Hole Using ‘Cosmic Magnifying Glass’

    Astronomers using two NASA space telescopes have found one of the farthest black holes from the solar system using a rare alignment of galaxies to act as a magnifying glass.

    Existing just 470 million years after the Big Bang—the event that’s thought to have created our universe 13.8 billion years ago—it’s the most distant black hole yet seen in X-rays. Neither wavelength of light is visible to humans.

    It was found using both the James Webb Space Telescope, which sees the universe in infrared, and the Chandra X-ray Observatory, which sees in x-rays.

    Remarkably Distant

    “We needed Webb to find this remarkably distant galaxy and Chandra to find its supermassive black hole,” said Akos Bogdan of the Center for Astrophysics, Harvard & Smithsonian (CfA), lead author of a paper published today in the journal Nature Astronomy. “We also took advantage of a cosmic magnifying glass that boosted the amount of light we detected.”

    The black hole—which is at an earlier stage of development that any found before—was found in a galaxy called UHZ1. It’s in a cluster of galaxies called Abell 2744, which exists about 3.5 billion light-years from the solar system. Nicknamed Pandora’s Cluster, Abell 2744 is a megacluster of three separate clusters of galaxies and a stunning 50,000 objects.

    However, JWST observations revealed UHZ1 to be behind the cluster, a whopping 13.2 billion light-years from the solar system. That makes it just 3% of the universe’s current age.

    Einstein Ring

    JWST was able to make the discovery thanks to gravitational lensing. It describes a scenario when the gravitational field of a foreground object is so intense that it distorts the space around it and bends the light from an object behind it into circular rings, both revealing its existence and magnifying it. It’s also known as an “Einstein Ring” because it was predicted by the famous physicist Albert Einstein.

    Gravitational lensing is the best way to infer the presence of incredibly distant objects and measure their mass. In this case the black hole’s mass is similar to that of its host galaxy—between 10 and 100 million times the mass of our sun. That’s way more mass than expected.

    Sure Signs

    Once JWST had located it, Chandra spent a couple of weeks observing X-ray light coming from the galaxy, a sure sign of a growing supermassive black hole. Acting as a gravitational lens, Abell 2744 magnified the background galaxy by a factor of four.

    The observations could help scientists figure out how black holes at the beginning of the universe appear to have grown so quickly. The paper suggests that this particularly black hole was born big.

    “There are physical limits on how quickly black holes can grow once they’ve formed, but ones that are born more massive have a head start,” said co-author Andy Goulding of Princeton University. “It’s like planting a sapling, which takes less time to grow into a full-size tree than if you started with only a seed.”

    Resource : https://www.forbes.com/sites/jamiecartereurope/2023/11/06/nasa-finds-record-breaking-black-hole-using-cosmic-magnifying-glass/?sh=2a9aefdf2324

    7Nov, 2023
    Google Launches Generative AI Training Resources on Google Cloud

    This initiative aims to empower individuals and organisations by equipping them with the necessary knowledge and skills to leverage the potential of Generative AI across various domains.

    Within the Google Cloud Skills Boost platform, two comprehensive learning paths have been unveiled:

    Introduction to Generative AI: This pathway caters to individuals in non-technical roles, encompassing areas such as sales, marketing, human resources, and operations.

    It offers introductory, non-technical courses tailored to provide a foundational understanding of Generative AI, its ethical considerations, and its influence on business processes.

    Generative AI for Developers: Targeted at software developers, machine learning engineers, and data scientists, this path provides technical, hands-on labs, and courses that delve deep into practical aspects of working with Generative AI. It also explores the use of Google Cloud services such as Generative AI Studio and Vertex AI.

    Both learning paths feature a blend of theoretical knowledge and practical experience, comprising video courses, hands-on labs, and opportunities to earn skill badges, which serve as valuable credentials for career advancement.

    Google has underscored its commitment to keeping these training materials up to date, ensuring their alignment with the latest developments in the rapidly evolving field of Generative AI.

    Introductory Path:

    • Google’s “Introduction to Generative AI” course is offered at no cost and can be completed in a mere 45 minutes.
    • This non-technical pathway covers the fundamental principles of responsible AI and elucidates how Generative AI can impact diverse facets of business operations.
    • Completing this path earns participants the coveted Generative AI Fundamentals skill badge, signifying their foundational knowledge of Google Cloud’s Generative AI technology.
    • The full introductory path, which includes additional courses like ‘Introduction to Giant Language Models’ and ‘Introduction to Responsible AI,’ typically takes approximately two hours to complete.

    Advanced Path:

    • Tailored for app developers, machine learning engineers, and data scientists, the “Generative AI for Developers” course demands learners to first complete prerequisite courses, namely “Introduction to Responsible AI” and “Generative AI Fundamentals.”
    • Notably, the advanced path is not offered for free; it requires the use of Google Cloud credits to access more advanced technical labs, including those related to Generative AI Studio and Vertex AI.
    • Subscribers to the Google Cloud Innovators Plus plan gain access to all Generative AI content and enjoy a full year of on-demand training through the Google Cloud Skills Boost program.

    Hands-On Experience With Labs:

    Google Cloud facilitates hands-on learning through a range of Generative AI labs, allowing learners to gain practical experience by directly engaging with the platform.

    Available labs, along with the corresponding number of Google Cloud credits required for participation, encompass:

    • Get Started with Generative AI Studio (one credit)
    • Generative AI with Vertex AI: Getting Started (one credit)
    • Generative AI with Vertex AI: Prompt Design (one credit)
    • Vertex AI PaLM API: Quick Start (one credit)
    • Integrate Search in Applications Using Gen AI App Builder (5 credits)
    • Prompt Design using PaLM (one credit)
    • Explore and Evaluate Models using Model Garden (5 credits)

    Collaboration With DeepLearning.AI:

    • In a collaborative effort with DeepLearning.AI, spearheaded by renowned AI expert Andrew Ng, Google Cloud offers an additional course titled “Understanding and Applying Text Embeddings with Vertex AI.”
    • This course is provided free of charge and offers valuable insights into various advanced AI applications, including classification, outlier detection, text clustering, and semantic search.

    Google’s commitment to democratising AI education and fostering innovation in Generative AI is evident through these comprehensive training resources, now accessible to a global audience through the Google Cloud Skills Boost platform.

    For more details on Google’s Generative AI training resources and access information, please visit the Google Cloud Skills Boost website.

    No-Cost Introduction to Generative AI Course

    The Introduction to Generative AI course is ideal for individuals who want to grasp the fundamentals of this transformative technology but have limited time to spare.

    In just 45 minutes, participants can gain a foundational understanding of Generative AI, its potential applications, and its ethical considerations.

    For those who find themselves hungry for more knowledge after completing the introductory course, Google offers the opportunity to continue their learning journey at no cost.

    By pursuing the extended learning path, learners can earn the prestigious Generative AI digital skill badge

    The skill badge encompasses not only the introductory course but also two additional courses:

    • Introduction to Large Language Models: This course delves into the intricacies of large language models, providing learners with insights into their capabilities and applications.
    • Introduction to Responsible AI: This course explores the ethical dimensions of AI and emphasizes responsible AI practices.

    In total, completing the Generative AI Fundamentals skill badge takes approximately two hours. Participants who successfully complete this extended learning path will be awarded a digital skill badge.

    The badge serves as a valuable credential that can be proudly displayed on resumes and shared across social media channels, certifying the individual’s foundational understanding of Google Cloud’s Generative AI technology.

    Innovators Plus Subscription

    A notable addition to Google’s offerings is the Innovators Plus Subscription. The subscription provides access not only to Generative AI content but also unlocks the entire Google Cloud Skills Boost catalog of on-demand training resources for an entire year.

    Priced at just $299, subscribers gain access to developer benefits worth $1,500, which includes up to $1,000 in Google Cloud credits.

    Key features of the Innovators Plus Subscription include:

    • Generative AI Content: Access to comprehensive Generative AI training materials that cover both introductory and advanced concepts.
    • Certification Support: Aspiring to be certified in Generative AI? Subscribers receive an exam voucher to help them achieve their certification goals.
    • Access to Experts: Enjoy special access to Google Cloud experts and executives, providing invaluable insights and guidance on Generative AI and related topics.
    • Live Learning Events: Participate in live learning events and webinars, allowing subscribers to interact with industry leaders and expand their knowledge.

    This subscription is designed to equip professionals with the skills and knowledge required to thrive in the rapidly evolving world of Generative AI. It offers an unparalleled opportunity to stay ahead in a highly competitive job market and to harness the full potential of Generative AI in various industries.

    Resource : https://www.techbusinessnews.com.au/news/google-launches-generative-ai-training-resources-on-google-cloud/

    6Nov, 2023
    Role of AI in management education

    With the rapidly accelerating use of artificial intelligence and machine learning across segments, b-schools are in the process of updating their curricula in these areas. They are also integrating AI into their teaching methodology.

    Sangeeta Shah Bhardwaj, professor (information management) at MDI Gurgaon, says that though there may be business leaders who are apprehensive about AI, they would have no choice but to adopt it when others start using AI-powered tools. MDI, which has a programme in business analytics, has also introduced courses like geospatial AI and AI in marketing. The geospatial AI course looks at the impact location data is having on organisations and in the startup ecosystem. It will introduce to students the principles for assessing or creating location AI-based products and interfaces.

    “AI is being built as part of the process of learning management systems,” says Bhardwaj. “For instance, if there is an online group discussion, AI-enabled LMS will evaluate how students performed and will bring out traits like aggressiveness among other behaviour. This can help a professor in engaging students better.” She adds that students can also get personalised feedback regarding their career path with the help of AI.

    However, while AI can be a good supplement, especially in quantifiable business processes such as finance, operations, supply chain and sales, it may not solve the complexities our business leaders face. “A few months ago, an American lawyer used AI to prepare a case,” says Vivek Suneja, dean, FMS Delhi. “When the case went to court, it had a lot of incorrect information. AI manufactured information; basically it falsified data.”

    Moreover, there are limitations to the use of AI in human behavioural processes and decisions such as people engagement, succession planning, leadership and values and culture. Human behaviour is not easy to predict. AI will keep developing and become more sophisticated, but it may not be able to replace human traits, says Vishal Talwar, director, IMT Ghaziabad. “Nowadays, the students are more evolved in using such technologies and how management institutions are able to harness and ride that particular wave is something that we have to decide,” he says.

    Business leaders will also have to prioritise technology so that investments are made in the areas where the impact is on the quality of decision-making. “We have to look at robots and human beings working together,” says Ranjan Banerjee, dean, BITSoM, Mumbai. “Robots handle programmable aspects, leaving judgment, empathy, insight and relational aspects to humans. Management education will view technology not as a business function but as a horizontal, embedding technology in function.”

    6Nov, 2023
    What did Chandrayaan-3 mission manage to accomplish?

    Chandrayaan-3 mission’s Vikram lander’s successful landing on August 23, in close proximity to the prime landing site at 69.37° south latitude and 32.35° east longitude, near the Manzinus U crater marked the highest-ever latitude achieved by a spacecraft during a soft moon landing. The European Space Agency’s (ESA’s) ESTRACK deep-space tracking station in New Norcia, Australia, played a crucial role in supporting the descent.

    Chandrayaan-3’s lander, Vikram, and rover, Pragyaan, conducted data and image collection in the region for approximately ten days before transmitting the gathered information back to Earth for analysis. Pragyan was put to sleep on September 2 and Vikram on September 4, while ISRO confirmed, “The rover has successfully fulfilled its designated tasks.”

    ISRO had expressed the hope that the lander could be reactivated at the end of a two-week lunar night when the rover and lander’s location would have been bathed in sunlight. The lunar sunrise had occurred on September 20 and the most favourable opportunity for Vikram and Pragyan to reactivate extended until September 22, spanning the initial three days.

    “Neither Vikram, nor Pragyan was equipped with systems to maintain warmth in temperatures that could drop to as low as -190°C. Sunlight started dissipating from the lunar surface again on September 30 even as ISRO scientists hoped to revive Vikram and Pragyan with the arrival of a new lunar day. Nevertheless, despite numerous attempts, Vikram and Pragyan remained dormant, with no signals received from Chandrayaan-3,” explained space expert Girish Linganna.

    Shortly before the rover was deactivated on September 2, ISRO mentioned that Pragyan had covered a distance of more than 100 metres and was still in motion—a remarkable journey for the six-wheeled rover, considering its speed of only 1 cm/s. Pragyan also avoided descending into craters scattered throughout the moon’s south polar region as its specialized mechanism allowed individual wheels to move independently, facilitating travel over various terrains.

    “The initial data set obtained from the lunar topsoil extending to a depth of 10 cm beneath the surface through a probe on the Vikram lander revealed a stark temperature contrast just above and below the surface. Surface temperatures were observed at nearly 60°C, while they sharply decreased below the surface, reaching -10°C at a depth of 80 mm,” remarked Linganna.

    The moon is known for its extreme temperature variations, with daytime temperatures near the lunar equator soaring to a scorching 120°C and nighttime temperatures plummeting to a frigid -130°C. In perpetually shadowed craters, where sunlight never reaches, temperatures as low as -250°C have been recorded. The wide temperature range is significant as it indicates that the moon’s soil (lunar regolith) serves as a highly effective insulator and could potentially be harnessed for constructing space colonies to regulate temperature and shield against radiation, making it a natural choice for habitat construction.

    “Pragyan’s laser detector that measured chemicals on the lunar surface traced various elements including aluminum, calcium, iron, chromium, titanium, manganese, silicon and oxygen. But the most significant element was sulphur, which typically originates from volcanoes, so contributing to our understanding of how the moon formed and evolved, besides its geographical features. Since sulphur serves as a valuable fertiliser, it is also promising news for potential plant growth if there is ever a lunar habitat,” said Lingana

    The Vikram lander was equipped with an instrument, the Instrument for Lunar Seismic Activity (ILSA), which not only monitored vibrations arising from its own research and experiments but also those generated by the rover and its operations. While ILSA was listening closely to lunar activities, it also recorded an event that seemed to be of natural origin and was actively investigating its source. It might have resulted from space debris, such as a meteorite or an asteroid impacting the lunar surface, or it could have been a seismic event, potentially marking the first Moonquake recorded since the 1970s.

    ISRO shared on X (formerly Twitter) that Vikram had conducted the “first-ever measurements of the near-surface lunar plasma environment” in the south polar region and determined it to be “relatively sparse”. Plasma denotes the existence of charged particles in the atmosphere, which could potentially interfere with the radio-wave communication employed by Chandrayaan-3. But the lunar plasma was found to be very thin, which implied that it would cause significantly less disruption to radio communication.

    The last action performed by Vikram before it was deactivated was referred to as a “hop experiment”. The lander was instructed to activate its engines, lifting off by approximately 40 cm and landing at a distance of 30-40 cm. This successful experiment, testing the engine’s capability to restart after a lunar landing, signified that the spacecraft could potentially be utilized in the future for such tasks as returning samples to Earth or supporting human missions. 

    Resource : https://www.theweek.in/news/sci-tech/2023/11/03/what-did-chandrayaan-3-mission-manage-to-accomplish.html

    4Nov, 2023
    Robot stand-in mimics movements in VR

    Researchers from Cornell and Brown University have developed a souped-up telepresence robot that responds automatically and in real-time to a remote user’s movements and gestures made in virtual reality.

    The robotic system, called VRoxy, allows a remote user in a small space, like an office, to collaborate via VR with teammates in a much larger space. VRoxy represents the latest in remote, robotic embodiment.

    Donning a VR headset, a user has access to two view modes: Live mode shows an immersive image of the collaborative space in real time for interactions with local collaborators, while navigational mode displays rendered pathways of the room, allowing remote users to “teleport” to where they’d like to go. This navigation mode allows for quicker, smoother mobility for the remote user and limits motion sickness.

    The system’s automatic nature lets remote teammates focus solely on collaboration rather than on manually steering the robot, researchers said.

    “The great benefit of virtual reality is we can leverage all kinds of locomotion techniques that people use in virtual reality games, like instantly moving from one position to another,” said Mose Sakashita, a doctoral student in the field of information science at Cornell. “This functionality enables remote users to physically occupy a very limited amount of space but collaborate with teammates in a much larger remote environment.”

    Sakashita is the lead author of “VRoxy: Wide-Area Collaboration From an Office Using a VR-Driven Robotic Proxy,” to be presented at the ACM Symposium on User Interface Software and Technology (UIST), held Oct. 29 through Nov. 1.

    VRoxy’s automatic, real-time responsiveness is key for both remote and local teammates, researchers said. With a robot proxy like VRoxy, a remote teammate confined to a small office can interact in a group activity held in a much larger space, like in a design collaboration scenario.

    For teammates, the VRoxy robot automatically mimics the user’s body position and other vital nonverbal cues that are otherwise lost with telepresence robots and on Zoom. For instance, VRoxy’s monitor — which displays a rendering of the user’s face — will tilt accordingly depending on the user’s focus.

    It is equipped with a 360-degree camera, a monitor that displays facial expressions captured by the user’s VR headset, a robotic pointer finger and omnidirectional wheels.

    In future work, Sakashita wants to supercharge VRoxy with robotic arms that would allow remote users to interact with physical objects in the live space via the robot proxy.

    This research was supported by the National Science Foundation and the Nakajima Foundation.

    Resource : https://www.sciencedaily.com/releases/2023/10/231027110752.htm

    4Nov, 2023
    How ‘blue’ and ‘green’ appeared in a language that didn’t have words for them

    The human eye can perceive about 1 million colors, but languages have far fewer words to describe those colors. So-called basic color terms, single color words used frequently by speakers of a given language, are often employed to gauge how languages differ in their handling of color. Languages spoken in industrialized nations such as the United States, for example, tend to have about a dozen basic color terms, while languages spoken by more isolated populations often have fewer.

    However, the way that a language divides up color space can be influenced by contact with other languages, according to a new study from MIT.

    Among members of the Tsimane’ society, who live in a remote part of the Bolivian Amazon rainforest, the researchers found that those who had learned Spanish as a second language began to classify colors into more words, making color distinctions that are not commonly used by Tsimane’ who are monolingual.

    In the most striking finding, Tsimane’ who were bilingual began using two different words to describe blue and green, which monolingual Tsimane’ speakers do not typically do. And, instead of borrowing Spanish words for blue and green, they repurposed words from their own language to describe those colors.

    “Learning a second language enables you to understand these concepts that you didn’t have in your first language,” says Edward Gibson, an MIT professor of brain and cognitive sciences and the senior author of the study. “What’s also interesting is they used their own Tsimane’ terms to start dividing up the color space more like Spanish does.”

    The researchers also found that the bilingual Tsimane’ became more precise in describing colors such as yellow and red, which monolingual speakers tend to use to encompass many shades beyond what a Spanish or English speaker would include.

    “It’s a great example of one of the main benefits of learning a second language, which is that you open a different worldview and different concepts that then you can import to your native language,” says Saima Malik-Moraleda, a graduate student in the Speech and Hearing Bioscience and Technology Program at Harvard University and the lead author of the study.

    Kyle Mahowald, an assistant professor of linguistics at the University of Texas at Austin, and Bevil Conway, a senior investigator at the National Eye Institute, are also authors of the paper, which appears this week in Psychological Science.

    Dividing up the color space

    In English and many other languages of industrialized nations, there are basic color words corresponding to black, white, red, orange, yellow, green, blue, purple, brown, pink, and gray. South American Spanish additionally divides the blue space into light blue (“celeste”) and dark blue (“azul”).

    Members of Tsimane’ society consistently use only three color words, which correspond to black, white, and red. There are also a handful of words that encompass many shades of yellow or brown, as well as two words that are used interchangeably to mean either green or blue. However, these words are not used by everyone in the population.

    Several years ago, Gibson and others reported that in a study of more than 100 languages, including Tsimane’, speakers tend to divide the “warm” part of the color spectrum into more color words than the “cooler” regions, which include blue and green. In the Tsimane’ language, two words, “shandyes” and “yushñus,” are used interchangeably for any hue that falls within blue or green.

    As a follow-up to that study, Malik-Moraleda wanted to explore whether learning a second language would have any effect on how the Tsimane’ use color words. Today, many Tsimane’ learn Bolivian Spanish as a second language.

    Working with monolingual and bilingual members of the Tsimane’, the researchers asked people to perform two different tasks. For the bilingual population, they asked them to do the tasks twice, once in Tsimane’ and once in Spanish.

    In the first task, the researchers showed the subjects 84 chips of different colors, one by one, and asked them what word they would use to describe the color. In the second task, the subjects were shown the entire set of chips and asked to group the chips by color word.

    The researchers found that when performing this task in Spanish, the bilingual Tsimane’ classified colors into the traditional color words of the Spanish language. Additionally, the bilingual speakers were much more precise about naming colors when they were performed the task in their native language.

    “Remarkably, the bilinguals really divide up the space much more than the monolinguals, in spite of the fact that they’re still primarily Tsimane’ speakers,” Gibson says.

    Strikingly, the bilingual Tsimane’ also began using separate words for blue and green, even though their native language does not distinguish those colors. Bilingual Tsimane’ speakers began to use “yushñus” exclusively to describe blue, and “shandyes” exclusively to describe green.

    Borrowing concepts

    The findings suggest that contact between languages can influence how people think about concepts such as color, the researchers say.

    “It does seem like the concepts are being borrowed from Spanish,” Gibson says. “The bilingual speakers learn a different way to divide up the color space, which is pretty useful if you’re dealing with the industrialized world. It’s useful to be able to label colors that way, and somehow they import some of that into the Tsimane’ meaning space.”

    While the researchers observed that the distinctions between blue and green appeared only in Tsimane’ who had learned Spanish, they say it’s possible that this usage could spread within the population so that monolingual Tsimane’ also start to use it. Another possibility, which they believe is more likely, is that more of the population will become bilingual, as they have more contact with the Spanish-speaking villages nearby.

    “Over time, these populations tend to learn whatever the dominant outside language is because it’s valuable for getting jobs where you earn money,” Gibson says.

    The researchers now hope to study whether other concepts, such as frames of reference for time, may spread from Spanish to Tsimane’ speakers who become bilingual. Malik-Moraleda also hopes to see if the color language findings from this study could be replicated in other remote populations, specifically, in the Gujjar, a nomadic community living in the Himalayan mountains in Kashmir.

    The research was funded by a La Caixa Fellowship, the Dingwall Foundation, the Intramural Research Program of the National Eye Institute, and the National Science Foundation CompCog Program.

    Resource : https://www.sciencedaily.com/releases/2023/11/231102135024.htm

    3Nov, 2023
    AI should be better understood and managed — new research warns

    Artificial Intelligence (AI) and algorithms can and are being used to radicalize, polarize, and spread racism and political instability, says a Lancaster University academic.

    Professor of International Security at Lancaster University Joe Burton argues that AI and algorithms are not just tools deployed by national security agencies to prevent malicious activity online, but can be contributors to polarization, radicalism and political violence — posing a threat to national security.

    Further to this, he says, securitization processes (presenting technology as an existential threat) have been instrumental in how AI has been designed, used and to the harmful outcomes it has generated.

    Professor Burton’s article ‘Algorithmic extremism? The securitization of Artificial Intelligence (AI) and its impact on radicalism, polarization and political violence’ is published in Elsevier’s high impact Technology in Society Journal.

    “AI is often framed as a tool to be used to counter violent extremism,” says Professor Burton. “Here is the other side of the debate.”

    The paper looks at how AI has been securitized throughout its history, and in media and popular culture depictions, and by exploring modern examples of AI having polarizing, radicalizing effects that have contributed to political violence.

    The article cites the classic film series, The Terminator, which depicted a holocaust committed by a ‘sophisticated and malignant’ artificial intelligence, as doing more than anything to frame popular awareness of Artificial Intelligence and the fear that machine consciousness could lead to devastating consequences for humanity — in this case a nuclear war and a deliberate attempt to exterminate a species.

    “This lack of trust in machines, the fears associated with them, and their association with biological, nuclear and genetic threats to humankind has contributed to a desire on the part of governments and national security agencies to influence the development of the technology, to mitigate risk and (in some cases) to harness its positive potentiality,” writes Professor Burton.

    The role of sophisticated drones, such as those being used in the war in Ukraine, are, says Professor Burton, now capable of full autonomy including functions such as target identification and recognition.

    And, while there has been a broad and influential campaign debate, including at the UN, to ban ‘killer robots’ and to keep the human in the loop when it comes to life-or-death decision-making, the acceleration and integration into armed drones has, he says, continued apace.

    In cyber security — the security of computers and computer networks — AI is being used in a major way with the most prevalent area being (dis)information and online psychological warfare.

    Putin’s government’s actions against US electoral processes in 2016 and the ensuing Cambridge Analytica scandal showed the potential for AI to be combined with big data (including social media) to create political effects centred around polarization, the encouragement of radical beliefs and the manipulation of identity groups. It demonstrated the power and the potential of AI to divide societies.

    And during the pandemic, AI was seen as a positive in tracking and tracing the virus but it also led to concerns over privacy and human rights.

    The article examines AI technology itself, arguing that problems exist in the design of AI, the data that it relies on, how it is used, and in its outcomes and impacts.

    The paper concludes with a strong message to researchers working in cyber security and International Relations.

    “AI is certainly capable of transforming societies in positive ways but also presents risks which need to be better understood and managed,” writes Professor Burton, an expert in cyber conflict and emerging technologies and who is part of the University’s Security and Protection Science initiative.

    “Understanding the divisive effects of the technology at all stages of its development and use is clearly vital.

    “Scholars working in cyber security and International Relations have an opportunity to build these factors into the emerging AI research agenda and avoid treating AI as a politically neutral technology.

    “In other words, the security of AI systems, and how they are used in international, geopolitical struggles, should not override concerns about their social effects.”

    As one of only a handful of universities whose education, research and training is recognised by the UK’s National Cyber Security Centre (NCSC), part of GCHQ, Lancaster is investing heavily in the next generation of cyber security leaders. As well as boosting the skills and talent pipeline in the region by building on its NCSC certified Masters degree with a new undergraduate degree in cyber security, it launched a trailblazing Cyber Executive Masters in Business Education.

    Resource : https://www.sciencedaily.com/releases/2023/11/231102135100.htm

    3Nov, 2023
    Lendlease, Google end development deals for San Francisco Bay Area projects

    Nov 3 (Reuters) – Lendlease Group (LLC.AX) and Alphabet’s (GOOGL.O) Google reached a mutual agreement to end development services deals for four master-planned districts in the San Francisco Bay Area, the Australian developer said on Friday.

    “The decision to end these agreements followed a comprehensive review by Google of its real estate investments, and a determination by both organisations that the existing agreements are no longer mutually beneficial given current market conditions,” Lendlease said.

    Google did not immediately reply to Reuters’ request for comment.

    Lendlease in 2019 bagged the contract from Google to develop $15 billion worth of residential and retail space in Sunnyvale, San Jose and Mountain View.

    Under the project, Leandlease was to develop up to 15 million square feet of residential, retail and hospitality space and associated amenities, and Google would develop office space.

    California’s commercial real estate market is one of the hardest hit globally as remote working has reduced the demand for office space amid declining property values and higher debt servicing costs on the back of rising rates.

    In June, Unibail-Rodamco-Westfield (URW.PA), owner of one of the biggest shopping centers in the city decided to walk away after 20 years, hurt by declining sales, occupancy and foot traffic.

    Earlier this year, Lendlease also paused its 47-storey Hayes Point project in central San Francisco, its largest investment in the Americas, looking to line up tenants or find a co-investor.

    Lendlease said it will remove the San Francisco Bay project, which was expected to commence construction in fiscal 2026, from its development pipeline.

    “While market expectations for the project had deflated over the past ~12-18 months, the change is negative for medium term (FY26-28) earnings,” analysts at UBS said.

    Lendlease retained its forecast for fiscal 2024, with core operating return on equity at the lower end of its 8%-10% range.

    Resource : https://www.reuters.com/world/us/lendlease-google-end-development-agreements-san-francisco-bay-area-projects-2023-11-03/

    30Oct, 2023
    Four Things to Consider as You Mature Your Threat Intel Program

    When ESG recently asked security professionals to identify the attributes of a mature threat intelligence program, the top response was “information dissemination with reports customized for consumption by specific individuals and groups”. However, many organizations don’t have mature threat intelligence programs and have yet to achieve this. ESG’s Jon Oltsik cites the 80/20 rule, where “80% of organizations have basic threat intelligence programs while only 20% are more advanced.”

    Sharing customized threat intelligence with key users is not just a sign that your threat intel program is maturing, it’s a great way to build deeper understanding, demonstrate value, and garner broader support for the program. If you want to begin, or improve, sharing customized intelligence with key users, consider these four aspects as you develop your process.

    1. Function. The threat intelligence team’s role is to provide products or services to many different internal customers, and each has different threat intel requirements to support their specific use cases. For example:

    • The security operations center (SOC) needs indicators of compromise that have been contextualized to show they are relevant and high priority so they can add them to their SIEM watchlist for monitoring.
    • Threat hunters need details of campaigns being run and adversaries’ motivations, targets and tactics, techniques and procedures (TTP), so they can look for activity that has bypassed defenses.
    • The incident response (IR) team needs threat intel around adversaries, campaigns and the infrastructure used so they can accelerate comprehensive response.
    • Vulnerability management teams need threat intel to help them understand their threat landscape and the likelihood of a vulnerability being exploited by adversaries that target the organization so they can prioritize patching.
    • Executive leadership at the business unit, C-suite and board levels need metrics that matter to them and that instills confidence that the organization is taking the right steps to maintain a strong security posture and is able to mitigate damage when an attack happens.

    2. Form. There is no “one way” to communicate. Different teams speak different languages and will apply threat intelligence in different ways, so it’s important to take the time to learn what type of communication will be most effective. For many technical teams actual feeds and dashboards work well, directly delivering the threat intel they need to do their specific jobs. Meanwhile, for executives and boards, a customized dashboard may work well for some and a PDF may be better for others. Either way, the content itself could be easily digestible and relevant to business leaders. Sticking with the typical metrics generated around number of events, alerts and incidents per month has far less impact than an update that contains the “who, what, when, where and why” of a thwarted attack, or whether or not they should be concerned about a recent attack that made the headlines.

    3. Frequency. Each team also has very different expectations and requirements when it comes to how often they need to receive threat intelligence. In security, the more time that passes, the more damage can be done. Additionally, many security teams are focused on being proactive, so speed is of the essence. But sharing data that hasn’t been vetted and contextualized for relevance to the organization ends up wasting valuable time. Threat intel teams can use automation to augment and enrich data with context, so teams get the right data faster and can easily prioritize it for analysis and action. 

    Executives and board members have different requirements. Establishing a regular schedule for more formal communications, at a minimum quarterly, is a good start. However, threat intel teams should also be prepared to field ad hoc questions when a new vulnerability or threat is in the news and the CEO asks: “What is it?”, “Does it pertain to us?”, “How are we impacted?” or “What are we doing to defend ourselves?”. 

    4. Feedback. Finally, it’s important to ask your different customers for feedback to make sure they are getting what they need, how and when they need it. Advancing your threat intelligence program is a two-way street. You need to hear how your service is being used and if it isn’t you need to understand why and adjust accordingly. Tweak the format, further customize the threat intel, change the frequency – do whatever it takes to ensure the program is delivering value and considered a crucial tool for each of your organization’s security teams and leadership.

    We’re halfway through 2023 and for many teams this is a good time to step back and measure progress against goals set at the beginning of the year. If one of your goals was to mature your threat intelligence program, conduct an honest assessment of how well you are doing at sharing threat intelligence with your different internal customers. There’s time to make relatively easy but high impact adjustments to showcase the value the threat intelligence program provides and turn it into a go-to resource that will strengthen your case for additional investment when budgeting season rolls around.

    Resource : https://www.securityweek.com/four-things-to-consider-as-you-mature-your-threat-intel-program/