Mediaboss Marketing

Keegan Hines, a former Georgetown adjunct professor and the current vice president of machine learning at Arthur AI, discussed the rapid rise in generative Artificial Intelligence (AI) programs and Georgetowns potential in adapting to software like ChatGPT.

The Master of Science in Data Science and Analytics program in the Graduate School of Arts & Sciences hosted the talk on March 17. The discussion centered on the rapid development of generative AI over the past six months.

Hines said generative AI has the capacity to radically change peoples daily lives, including how students are taught and how entertainment is consumed.

I definitely think were going to see a lot of personal tutoring technologies coming up for both little kids and college students, Hines said at the event. I have a feeling that in the next year, someone will try to make an entirely AI-generated TV show. Its not that hard to imagine an AI-generated script, animation and voice actors.

Imagine what Netflix becomes. Netflix is no longer recommend Keegan the best content; Netflix is now create something from scratch which is the perfect show Keegans ever wanted to see, Hines added.

Hines then discussed algorithms that generate text. He said the principal goal of these algorithms is to create deep learning systems that can understand complex patterns over longer time scales.

Hines said one challenge AI faces is that it can provide users with incorrect information.

These models say things and sometimes theyre just flatly wrong, Hines said. Google got really panned when they made a product announcement about Bard and then people pointed out Bard had made a mistake.

Bard, Googles AI chatbot, incorrectly answered a question about the James Webb Space Telescope in a video from the programs launch Feb. 6, raising concerns about Googles rushed rollout of Bard and the possibility for generative AIs to spread misinformation.

Hines said the potential for bias and toxicity in AI is present, as seen with Microsofts ChatGPT-powered Bing search engine, which manufactured a conspiracy theory relating Tom Hanks to the Watergate scandal.

Theres been a lot of research in AI alignment, Hines said. How do we make these systems communicate the values we have?

Teaching and learning in all levels of education will need to adapt to changes in technology, according to Hines.

One example is a high school history teacher who told students to have ChatGPT write a paper and then correct it themselves, Hines said. I think this is just the next iteration of open book, internet, ChatGPT. How do you get creative testing someones critical thinking on the material?

Hines said OpenAI, the company behind ChatGPT, noticed larger, more complex language models were more accurate than smaller models due to lower levels of test loss or errors made during training.

A small model has a high test loss whereas a really big model has a much more impressive test loss, Hines said. The big model also requires less data to reach an equivalent amount of test loss.

OpenAIs hypothesis was that the secret to unlocking rapid advancement in artificial intelligence lies in creating the largest model possible, according to Hines.

There didnt seem to be an end to this trend, Hines said. Their big hypothesis was, lets just go crazy and train the biggest model we can think of and keep going. Their big bet paid off and these strange, emergent, semi-intelligent behaviors are happening along the way.

Hines said he is optimistic about the fields future, and he predicted AI will be able to produce even more complex results, such as creating a TV show. It was really only about ten years ago that deep learning was proven to be viable. Hines said. If were going to avoid the dystopian path and go down the optimistic path, generative AI will be an assistant. It will get you 80% of the way and you do the next 20%.

Visit link:
Machine Learning Executive Talks Rise, Future of Generative AI - Georgetown University The Hoya

TORONTO, ON / ACCESSWIRE / March 28, 2023 / New Acre Project announces a partnership between ALUS and Albo Climate to use a remote sensing-based platform for identifying carbon stocks and sequestration for tree and shrub projects. The ultra high-resolution, AI-powered products will be calibrated for ALUS' tree and shrub projects in four provinces in Canada: Ontario, Alberta, Saskatchewan and Quebec. The sites are located on private lands managed and maintained by farmers and ranchers participating in the ALUS program. New Acre Project is ALUS' corporate ESG investment platform, which enables corporations to invest in projects that produce carbon sequestration, water, biodiversity and other social and environmental benefits, which will be bundled in an ecosystem credit.

ALUS tree and shrub project sites serve not only as carbon sinks, but also foster local biodiversity and reduce runoff of agricultural inputs into local watersheds. Most projects are developed on the edges of the farmers' fields which are oftentimes underutilized and include a variety of tree species that are native and appropriate to the area, including spruce, pine and maple.

"We are excited about our partnership with ALUS, a fantastic organization massively implementing nature-based solutions across Canada. We are looking forward to applying our modes to service their afforestation, reforestation and regeneration projects, and detect carbon stocks in the woody vegetation even in small patches with recently planted trees," notes Marco Calderon, CTO of Albo Climate.

Albo Climate's innovative technology analyzes satellite imagery using deep learning to map, measure and monitor carbon sequestration, deforestation and land-use changes in nature-based climate solutions.

"We are delighted to be partnering with Albo Climate, an innovative start-up developing robust remote-sensing solutions suited to our needs. We are confident that this kind of technology has immense potential to scale up and create transparency in a variety of nature-based climate solutions," says Mary-Ellen Anderson, Head of Special Projects and Innovation at ALUS.

About New Acre ProjectTM

ALUS' New Acre Project is a corporate investment platform designed to help purpose-driven corporations go beyond their sustainability objectives and invest in the next-generation of conservation to generate positive impacts in the communities where they operate. Through New Acre Project, corporations are enabling these communities to become more resilient and empowering farmers and ranchers to build nature-based solutions on their land, one acre at a time. Learn more at newacre.org.

About Albo Climate

Albo Climate, headquartered in Tel Aviv, Israel, supports Nature-Based climate solutions with AI and Satellite-Powered technology. They are currently servicing a number of forestry and agriculture projects around the world, including in the USA, Canada, Ecuador, Peru, Germany, Australia and Cameroon. Learn more at albosys.com

Contact Persons:

ALUS and New Acre Project: Nadine Mercure, Director of Communications, [emailprotected]

Albo Climate: Ariella Charny, COO, [emailprotected] Marco Calderon, CTO, [emailprotected]

Tree planting project (2022) in ALUS Monteregie community, Quebec, Canada. (photo: courtesy of ALUS)

View additional multimedia and more ESG storytelling from New Acre Project on 3blmedia.com.

Contact Info:Spokesperson: New Acre ProjectWebsite: https://www.3blmedia.com/profiles/new-acre-projectEmail: [emailprotected]

SOURCE: New Acre Project

Read the original here:
New Acre Project Leverages Remote Sensing and Machine ... - AccessWire

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