Mediaboss Marketing

In today's digital age, the widespread use of social media and online communication has brought new challenges, including the rise of cyberbullying.

With the anonymity and accessibility of the internet, individuals may engage in harassing or intimidating behaviour online, leading to devastating consequences for victims.

However, technological advancements such as machine learning offer hope in improving the efficiency of detecting and preventing cyberbullying.

Machine learning is a powerful tool within the field of artificial intelligence that allows machines to learn and enhance their performance without explicit programming.

Specifically, machine learning algorithms can be trained to detect patterns within online communication that may indicate cyberbullying behaviour.

These algorithms can identify instances of cyberbullying in real time by analysing vast amounts of data gathered from social media platforms, messaging apps, and other online platforms.

This paves the way for prompt intervention and prevention measures.

"One application of machine learning that can help identify cyberbullying is natural language processing [NLP], says Associate Professor Manjeevan Singh, from the School of Business atMonash University Malaysia.

NLP algorithms can analyse the language used in online communication to determine the tone and sentiment of the message, as well as identify specific terms or phrases associated with bullying behaviour.

For example, if an individual frequently uses foul language or makes threatening statements, the algorithm may flag it as potentially abusive behaviour, and alert the appropriate authorities."

According to Dr Manjeevan, using machine learning for the identification of cyberbullying offersnumerous advantages, particularly in terms of scalability.

Conventional ways of preventing cyberbullying, such as manually monitoring online platforms, can be inefficient and time-consuming, particularly for major social media sites that have millions of users.

In contrast, machine learning algorithms enable the recognition and response to cyberbullying incidents in a timely and effective manner.

However, this approach also presents certain challenges. In order to train the algorithms, significant quantities of high-quality data are required, which is one of the most challenging aspects.

Although cyberbullying is rife, it remains a relatively unexplored area, particularly in the context of the Malay language. Theres a dearth of publicly accessible datasets containing hate speech, which poses a challenge for researchers.

To address this issue, efforts were made to collect tweets in Malay, which were then processed to remove any tweets in related languages, such as Indonesian, that had been mixed in. While this effort began with several thousand tweets, it represents an important starting point for further research.

After manually labelling each tweet as bullying or not, it was found that almost 40% of the selected dataset was marked as bullying.

"To classify the tweets, we experimented with several deep-learning models, including Bert, XLnet, and Fasttext. The F1 scores for XLnet outperformed Bert, with an achieved classification accuracy of 76%. By incorporating both XLnet and Fasttext, the accuracy rate increased to 80%," Dr Manjeevan stated.

It was acknowledged that the accuracy rates could be further improved with additional training and the incorporation of hate speech data.

To help researchers move more quickly through their research, this dataset will be made publicly available.

Dr Manjeevan added its likely therell be an increase in the utilisation of machine learning technology to identify and prevent cyberbullying as the technology continues to advance. Cyberbullying can be reduced, and a more secure environment can be created for all internet users if the appropriate tools and strategies are used.

Experimental design and implementation was carried with Associate Professor Sriparna Saha, Shaubhik Bhattacharya, and Krishanu Maity, from the Department of Computer Science and Engineering, at the Indian Institute of Technology, Patna.

See more here:
Machine learning to the rescue: Preventing cyberbullying in real ... - Monash Lens

Position: Co-founder, CEO

Founders: Alon Lev, Lior Penso, Yuval Fernbach, Ran Romano

Company description: Qwak simplifies the deployment and integration of machine learning at scale. Qwaks ML Platform empowers data science and ML engineering teams to unblock the full realization of machine learning for the business. By abstracting the complexities of model deployment, integration and optimization, Qwak brings agility and high-velocity to all ML initiatives designed to transform business, innovate and create competitive advantage.

Amount raised: $27 million

Investors: Bessemer, Leaders Fund, StageOne, Amiti

Qwak was part of the Israeli startup squad that participated in Calcalist's Mind the Tech London 2023 conference. Calcalist's "Dream Team" to London included early-stage startup companies in various fields. The startups joined the official delegation in its journey to London and took part in roundtable discussions at the event, presenting their companies to senior executives from the British and international tech industries.

Originally posted here:
Qwak: Simplifying deployment and integration of machine learning ... - CTech

Raspberry Pi has been at the forefront of single-board computers (SBCs) for quite some time. However, nearly four years after the launch of Raspberry Pi 4, a new model is on the horizon.

Previous Raspberry Pi iterations generally involved faster processors, more RAM, and with the Pi 4, improved IO. However, a lot of Pis are used for AI (artificial intelligence) and ML (machine learning) purposes, leading to a lot of speculation from DIY enthusiasts about the Raspberry Pi 5's built-in machine learning capabilities.

Whether the Raspberry Pi 5 gets built-in machine learning capabilities depends a lot on what CPU the board is based around. Raspberry Pi co-founder Eben Upton teased the future of custom Pi silicon back at the tinyML Summit 2021. Since then, an imminent Raspberry Pi 5 release with massive improvements to ML is looking very likely.

Up until Raspberry Pi 4, the development team had been using ARM's Cortex processors. However, with the release of the Raspberry Pi Pico in 2021 came the RP2040, the company's first in-house SoC (system-on-chip). While it doesn't have the same power as the Raspberry Pi Zero 2 W, one of the cheapest SBCs on the market, it does provide microcontroller capabilities similar to that of an Arduino.

The Raspberry Pi 2, Pi 3, and Pi 4 have used ARM's Cortex-A7, Cortex-A53, and Cortex-A72 processors respectively. These have increased the Pi's processing capabilities over each generation, giving each progressive Pi more ML prowess. So does that mean we'll see built-in machine learning on the Raspberry Pi 5's CPU?

While there's no official word on what processor will power the Pi 5, you can be pretty sure it'll be the most ML-capable SBC in the Raspberry Pi lineup and will most likely have built-in ML support. The company's Application Specific Integrated Circuit (ASIC) team has been working since on the next iteration, which seems to be focused on lightweight accelerators for ultra-low power ML applications.

Upton's talk at tinyML Summit 2021 suggests that it might come in the form of lightweight accelerators likely running four to eight multiply-accumulates (MACs) per clock cycle. The company has also worked with ArduCam on the ArduCam Pico4ML, which brings together ML, a camera, microphones, and a screen into a Pico-sized package.

While all the details about the Raspberry Pi 5 aren't yet confirmed, if Raspberry Pi sticks to its trend of incrementally upgrading its boards, the upcoming SBC can be a rather useful board that'll check a lot of boxes for ML enthusiasts and developers looking for cheap hardware for their ML projects.

The Raspberry Pi 5 could come with built-in machine learning support, which opens up a plethora of opportunities for just about anyone to build their own ML applications with hardware that's finally able to keep up with the technology without breaking the bank.

You can already run anything from a large language model (LLM) to a Minecraft server on existing Raspberry Pis. As the SBC becomes more capable (and accessible), the possibilities of what you can do with a single credit-card-sized computer will also increase.

The rest is here:
Will the Raspberry Pi 5 CPU Have Built-in Machine Learning? - MUO - MakeUseOf

Through its portfolio, Airport management software company AeroCloud strives to provide customers with a crystal ball, managing airports in real-time as well as predicting passenger flow and gate optimisation.

The company is developing a reputation for introducing new technology and being customer centric when offering solutions.

George Richardson, CEO of AeroCloud, has spoken with Airport Technology about the companys strategy, challenges and opportunities in the sector, and the importance of business models.

Jasleen Mann: How and when was AeroCloud founded?

George Richardson: AeroCloud was founded in Macclesfield in the North West of England in 2019 by myself and my co-founder and our CTO, Ian Forde-Smith. He has worked in the airport sector his entire career, and I was a retired racing driver, having driven competitively for 10 years.

I was excited at the prospect of bringing together Ians extensive sector and technical knowledge and my commercial mindset to create solutions that enable the airport sector to seize on the benefits of the cloud.

We built a matchbox business plan designed to compete with and displace one of five legacy operators in the airport space. Were a typical David and Goliath story and its been a tremendous journey since we first launched.

Were forever grateful that our first customers at Northwest Florida Beaches International Airport and Tampa International Airport trusted in us. And to our investors for taking the time to understand the airport technology sector and subsequently getting as excited as we are about the unforeseen potential we can bring to airports as a SaaS company.

JM: What are AeroClouds key areas of focus?

GR: AeroCloud is an intelligent management platform designed for the airport sector and the only cloud-native player in this space. The platform enables everything from faster passenger processing times to improved self-service check-in and bag drop and facilitates, increased communication between stakeholders to deal with real-time fluctuations in processes to ensure that airports work better and communicate with their customers.

We recently also launched an industry-first computer vision solution for airports that offers kerb to gate insights for the first time. AeroCloud Optic uses computer vision to intelligently, anonymously and accurately track passengers as they move through an airport. The real-time monitoring of passengers triggers alerts in response to operational bottlenecks such as extended wait times at security, which can then be immediately addressed.

The AI and machine learning algorithms also allow airport staff to identify trends and predict future scenarios to inform more accurate decision-making and long-term planning. This enables better resource management and enhanced retail opportunities for concession partners, which in turn improves the airports passenger experience.

JM: What are the challenges in this area?

GR: Airports are fast-moving complex domains, requiring smooth coordination of multiple factors in a high-pressure environment from security to passengers and airlines. A lack of synergy between these different factors can affect an airports performance in some instances, operates at only around 66% capacity.

We want to solve problems the aviation industry has struggled with for decades due to the reliance on on-site legacy technology, which isnt fit for purpose because its clunky, needs on-site maintenance, and doesnt take advantage of the latest technological innovations enabled through the cloud.

JM: What opportunities have you embraced?

GR: We have put in so much hard work to ensure that what we bring to the market is revolutionary for our sector, leveraging new technologies and practices to solve issues that have existed for decades.

We have also created an innovative ecosystem of evangelical customers, and meaning we invite them to tell our product team their problems. We then collaborate with them to find solutions and that informs the features that we create and deliver. This means that we remain customer centric across every business area, from product design and development through to customer support.

Our mission is to be the largest provider of airport operation automation software for the small to medium-sized airport market globally. In February 2023, we successfully raised $12.6million in Series A funding after we were able to demonstrate our commitment to and progress against this goal.

Indeed, when commenting on the motivation behind investing in AeroCloud, one of our new investors, Liz Christo at Stage 2 Capital, said In only a small time, AeroCloud has become the definitive operating software for small to medium-sized airports. With this new funding, we plan to deliver on our bold ambitions to expand our business, employ local people in the North West where we were founded, and continue to displace our competitors.

JM: What is the importance of democratisation of data across airports?

GR: Coordination and communication are key to operational management. Yet in many airports, most stakeholders are in the dark about the current state of play. Data is not readily accessible and many third parties might never see it beyond periodical reports.

Putting data in the hands of all relevant parties helps them understand how their services are performing and how that impacts the airport operations as a whole. That is why we offer unlimited licenses to our cloud-based platform. We dont want airports to have to choose who has access to data nor reduce its potential in supporting better operational decision-making.

JM: How does the companys strategy differ to competitors?

GR: The opportunity, we think, is a $20 billion market in which legacy players dominate; AeroCloud is the only 100% cloud-native supplier, and we are shaking up the status quo.

We can centralise all an airports operational data and flight data in about 48 hours. Our data also operates in real-time and is up to 30% more accurate than our competitors. When airports require updates or issues fixed, we deliver these via the cloud, saving resourcing and money unlike our legacy competitors who have to send a technician on site.

We also enable unlimited licenses per customer so an airports entire stakeholder base can access to the platform at no extra cost. This means AeroCloud can be used on any device wherever an airports team is based, whether thats onsite or remote that ensures the platform is more secure than legacy systems which are often run on centrally located stack servers.

JM: How do airport business models now compare to pre-pandemic models?

GR: Even before the 2020 Covid-19 pandemic, which caused escalating passenger processing times and labour shortages, these issues were difficult to manage, particularly for small and medium-sized airports that dont have the budgets and capacities that their larger peers do. As travel returns to pre-pandemic levels, airports have struggled with adjusting to heightened demand, affecting operations worldwide.

And while it is a difficult time still for many of their airports as they whole industry faces due to the debts that arose during the pandemic, it is the time when they need to invest in improving the operational efficiencies of their airports to help them boost passenger experience and revenue in the long-term.

JM: What are the implications of the 80:20 rule?

GR: The 80:20 rule requires airlines to use 80% of their take-off and landing slots or risk losing them to a competitor the following year. The rule was relaxed during the pandemic after IATA highlighted the changing schedules that many airlines were facing.

From an airport perspective, the reintroduction of this rule will necessitate a seamless journey of passengers through the terminal so that they arrive to their gate on time and support airlines to leave within their allotted timing slot.

Read more:
Enhancing airport operations through cloud-native technology, AI ... - Airport Technology

Wealthmanagement is a complex and constantly evolving field, with a vast amount ofdata to analyze and complex decisions to make. With the rise of artificialintelligence (AI) and machine learning (ML), the field of wealth management hasexperienced a significant transformation in recent years.

In thisarticle, we will explore the benefits of AI and ML in wealth management,including customized portfolios and predictive analytics.

One of the mostsignificant benefits of AI and ML in wealth management is the ability to createcustomized portfolios for clients. Traditionally, wealth managers relied onmanual analysis and intuition to create investment portfolios for theirclients.

This processwas time-consuming, costly, and often resulted in portfolios that were notfully optimized for the client's unique financial situation and goals.

Keep Reading

AI and MLtechnologies can analyze vast amounts of data quickly and accurately, providingwealth managers with the insights needed to create customized investmentportfolios that meet the unique needs of each client.

Thesetechnologies can analyze factors such as risk tolerance, investment goals, andfinancial situation to create a portfolio that is tailored to the client'sspecific needs.

In addition, AIand ML can continually monitor the portfolio and adjust it as needed to ensurethat it remains aligned with the client's goals and objectives. This can helpto optimize portfolio performance and reduce the risk of losses due to marketfluctuations or other factors.

Anothersignificant benefit of AI and ML in wealth management is the ability to usepredictive analytics to make more informed investment decisions.

Predictiveanalytics involves using historical data and machine learning algorithms tomake predictions about future market trends and asset performance.

By analyzingvast amounts of data, including economic indicators, market trends, and assetperformance, AI and ML technologies can provide wealth managers with insightsand predictions that would be impossible to obtain through manual analysisalone.

Thesetechnologies can identify patterns and trends in the data that humans maynot be able to detect, providing wealth managers with a more comprehensive andaccurate view of the market.

Thisinformation can be used to make more informed investment decisions, such aswhich assets to invest in and when to buy or sell them. Predictive analyticscan also help wealth managers to identify potential risks and opportunities,allowing them to make proactive decisions to mitigate risk and capitalize onmarket opportunities.

While AI and MLtechnologies offer significant benefits for wealth management, there are alsochallenges and considerations to keep in mind. One of the primary challenges isensuring the accuracy and reliability of the data used to train the machinelearning algorithms.

If the data isbiased or incomplete, the algorithms may produce inaccurate or unreliablepredictions, leading to poor investment decisions and potential losses.

Anotherconsideration is the ethical and regulatory implications of using AI and ML inwealth management. As these technologies become increasingly sophisticated, itis essential to ensure that they are used ethically and in compliance withregulatory requirements.

This includesconsiderations such as data privacy, transparency, and accountability.

AI has theability to analyze large sets of data and provide insights that humans may notbe able to uncover. However, as with any technology, there are risks involved,and AI can backfire on wealth management in several ways.

One of the mostsignificant risks of using AI in wealth management is the potential for biasedalgorithms. AI is only as good as the data it is trained on, and if the data isbiased, the algorithms will also be biased. This can lead to unequal treatment ofclients and inaccurate investment decisions. For example, if the AI algorithmis trained on data that disproportionately represents wealthy individuals, itmay not be able to accurately predict the needs and goals of lower-incomeclients.

Moreover, AIrelies on historical data to make predictions about the future, and if thatdata is biased, the algorithm will also be biased. Biased algorithms can leadto inaccurate predictions and investment decisions, which can result infinancial losses for clients. For example, an algorithm trained on historicaldata that disproportionately represents a certain industry or demographic maynot be able to accurately predict the performance of other industries ordemographics.

While AI cananalyze vast amounts of data quickly, it cannot replace human expertise andjudgment entirely. Overreliance on technology can lead to missed opportunitiesor suboptimal investment decisions. A combination of human expertise andAI-powered analytics can lead to better investment decisions, but it isimportant to strike a balance between the two.

There is a riskthat AI can reinforce existing inequalities in wealth management. Wealthmanagement firms that use AI may be more likely to cater to wealthy clients whocan afford their services while ignoring lower-income clients. This can createa vicious cycle where wealthy clients continue to benefit from AI-poweredwealth management services, while those with less wealth are left behind.

AI and MLtechnologies are transforming the field of wealth management, providing wealthmanagers with new insights and capabilities to create customized portfolios andmake more informed investment decisions.

By analyzingvast amounts of data and using predictive analytics, these technologies canhelp wealth managers to optimize portfolio performance, reduce risk, andcapitalize on market opportunities.

However, it isessential to keep in mind the challenges and considerations associated withusing AI and ML in wealth management.

Wealth managersmust ensure the accuracy and reliability of the data used to train the machinelearning algorithms and consider the ethical and regulatory implications ofusing these technologies.

Overall, AI andML have the potential to revolutionize the field of wealth management andprovide significant benefits for both wealth managers and their clients. Asthese technologies continue to evolve, it is essential for wealth managers tostay informed and embrace them to remain competitive in a rapidly evolvingindustry.

Wealthmanagement is a complex and constantly evolving field, with a vast amount ofdata to analyze and complex decisions to make. With the rise of artificialintelligence (AI) and machine learning (ML), the field of wealth management hasexperienced a significant transformation in recent years.

In thisarticle, we will explore the benefits of AI and ML in wealth management,including customized portfolios and predictive analytics.

One of the mostsignificant benefits of AI and ML in wealth management is the ability to createcustomized portfolios for clients. Traditionally, wealth managers relied onmanual analysis and intuition to create investment portfolios for theirclients.

This processwas time-consuming, costly, and often resulted in portfolios that were notfully optimized for the client's unique financial situation and goals.

Keep Reading

AI and MLtechnologies can analyze vast amounts of data quickly and accurately, providingwealth managers with the insights needed to create customized investmentportfolios that meet the unique needs of each client.

Thesetechnologies can analyze factors such as risk tolerance, investment goals, andfinancial situation to create a portfolio that is tailored to the client'sspecific needs.

In addition, AIand ML can continually monitor the portfolio and adjust it as needed to ensurethat it remains aligned with the client's goals and objectives. This can helpto optimize portfolio performance and reduce the risk of losses due to marketfluctuations or other factors.

Anothersignificant benefit of AI and ML in wealth management is the ability to usepredictive analytics to make more informed investment decisions.

Predictiveanalytics involves using historical data and machine learning algorithms tomake predictions about future market trends and asset performance.

By analyzingvast amounts of data, including economic indicators, market trends, and assetperformance, AI and ML technologies can provide wealth managers with insightsand predictions that would be impossible to obtain through manual analysisalone.

Thesetechnologies can identify patterns and trends in the data that humans maynot be able to detect, providing wealth managers with a more comprehensive andaccurate view of the market.

Thisinformation can be used to make more informed investment decisions, such aswhich assets to invest in and when to buy or sell them. Predictive analyticscan also help wealth managers to identify potential risks and opportunities,allowing them to make proactive decisions to mitigate risk and capitalize onmarket opportunities.

While AI and MLtechnologies offer significant benefits for wealth management, there are alsochallenges and considerations to keep in mind. One of the primary challenges isensuring the accuracy and reliability of the data used to train the machinelearning algorithms.

If the data isbiased or incomplete, the algorithms may produce inaccurate or unreliablepredictions, leading to poor investment decisions and potential losses.

Anotherconsideration is the ethical and regulatory implications of using AI and ML inwealth management. As these technologies become increasingly sophisticated, itis essential to ensure that they are used ethically and in compliance withregulatory requirements.

This includesconsiderations such as data privacy, transparency, and accountability.

AI has theability to analyze large sets of data and provide insights that humans may notbe able to uncover. However, as with any technology, there are risks involved,and AI can backfire on wealth management in several ways.

One of the mostsignificant risks of using AI in wealth management is the potential for biasedalgorithms. AI is only as good as the data it is trained on, and if the data isbiased, the algorithms will also be biased. This can lead to unequal treatment ofclients and inaccurate investment decisions. For example, if the AI algorithmis trained on data that disproportionately represents wealthy individuals, itmay not be able to accurately predict the needs and goals of lower-incomeclients.

Moreover, AIrelies on historical data to make predictions about the future, and if thatdata is biased, the algorithm will also be biased. Biased algorithms can leadto inaccurate predictions and investment decisions, which can result infinancial losses for clients. For example, an algorithm trained on historicaldata that disproportionately represents a certain industry or demographic maynot be able to accurately predict the performance of other industries ordemographics.

While AI cananalyze vast amounts of data quickly, it cannot replace human expertise andjudgment entirely. Overreliance on technology can lead to missed opportunitiesor suboptimal investment decisions. A combination of human expertise andAI-powered analytics can lead to better investment decisions, but it isimportant to strike a balance between the two.

There is a riskthat AI can reinforce existing inequalities in wealth management. Wealthmanagement firms that use AI may be more likely to cater to wealthy clients whocan afford their services while ignoring lower-income clients. This can createa vicious cycle where wealthy clients continue to benefit from AI-poweredwealth management services, while those with less wealth are left behind.

AI and MLtechnologies are transforming the field of wealth management, providing wealthmanagers with new insights and capabilities to create customized portfolios andmake more informed investment decisions.

By analyzingvast amounts of data and using predictive analytics, these technologies canhelp wealth managers to optimize portfolio performance, reduce risk, andcapitalize on market opportunities.

However, it isessential to keep in mind the challenges and considerations associated withusing AI and ML in wealth management.

Wealth managersmust ensure the accuracy and reliability of the data used to train the machinelearning algorithms and consider the ethical and regulatory implications ofusing these technologies.

Overall, AI andML have the potential to revolutionize the field of wealth management andprovide significant benefits for both wealth managers and their clients. Asthese technologies continue to evolve, it is essential for wealth managers tostay informed and embrace them to remain competitive in a rapidly evolvingindustry.

Original post:
AI and Machine Learning in Wealth Management: Customized Portfolios, Predictive Analytics - Finance Magnates