Mediaboss Marketing

Avalo's machine-learning tech speeds the development of new crops.

Climate change is a big problem that requires big solutions, but one place where it might have an unexpected impact is on your dinner plate. By making climate-resilient crops with the help of machine-learning approach, Durhams Avalo looks to keep those plates full.

The companys Chief Scientific Officer, Mariano Alvarez, will present tomorrow (March 29) at this years Venture Connect summit in RTP.

Like many great ideas, Avalo was conceived between a pair of friends over a couple of pints. Scientist-turned-entrepreneur Brendan Collins finally convinced his friend, Duke post-doc researcher Alvarez, that his research on plant genetics could do a lot more good in the real world than in the lab.

Over a series of happy hour beers, he convinced me that it would be way more fun to start a startup and do research in that setting than to continue to apply for faculty positions, Alvarez said. So in 2020, we did just that. And he was totally rightits way more fun.

Alvarez is used to tackling plant genetics in light of climate crises. In the wake of the Deepwater Horizon oil spill crisis in 2010, he completed PhD research at the University of South Florida looking at how plants reacted to the dramatic environmental changes.

This research brought the young scientist to Duke for post-grad study on understanding the relationship between plant genomes and the environment. Guided by Duke computer science professor Cynthia Rudin, Alvarez soon realized that machine learning and computational methods could solve many of the problems in identifying genes that are meaningful to plant environmental resilience.

Around the same time that the Duke duo figured out how to use machine learning as an impactful crop-development tool, Alvarez and Collins began doing market research. The need for faster crop development was urgent, they foundand with climate changes biggest effects just decades away, the time was ripe to launch their startup.

A lot of people dont realize just how long it takes to come up with a new variety of crops, Alvarez said. You go to the store, theres different types of tomatoes, theres different types of cucumbers, and you sort of imagine that theyre all just sitting around. It actually takes a long time for somebody to develop those varieties, anywhere between seven and 15 years. Its roughly a $200 million process to actually get them through trials and into farmers fields.

Collins, who is Avalos CEO as well as co-founder, brought his software-scaling skills from previous startup ventures, allowing them to translate this computational model into a marketable product.

Using its computational model, Avalo can rapidly test for genes that may produce a desired phenotypic outcome in a plant. The companys computational engine allows them to discover the genetic basis of complex traits, even from patchy data.

This not only makes the process of developing new crops much faster, it also makes it cheaper by slashing the number of years needed for research and development.

Traditionally, crop development has focused on traits that will make a process that takes 15 years and $200 million worth their while, usually aiming for genetic variations that lead to high yield or herbicide resistance. With Avalos technology, companies can focus on other traits, like ones that make a crop able to grow effectively in the new temperatures that result from climate change, or even tweaking a crop for better taste. This technology is perfect for a diverse industry with diverse needs.

One thing that was really interesting going into agriculture is just the scope of all of the things that people are looking for, and how diverse the agricultural system is, Alvarez said. And how unique growers needs are.

Avalo offers three buckets of product, Alvarez says. In the computational bucket falls Avalos work in providing their computational tools to companies who know what traits they want but dont have the technology to make it happen. In the second bucket, Avalo transfers specific traits into a plant for a customer.

In the third bucket is Avalos front-to-back operations. The company is currently working on a heat-tolerant variety of Chinese broccoli, for example, but their capabilities are not crop-limited, especially with the help of their three greenhouses spread across the Triangle.

The company is growing fast, but then again, so is the problem it looks to addressclimate change waits for no man, or machine.

We really only have about 30 more years until some of the biggest changes start to take effect, Alvarez said. If development takes 15 years, then we only have two shots, which is just not enough time to develop the varieties that we think were going to need to to adapt an entire agricultural system to a whole new climate.

See original here:
Durham's Avalo Uses Machine Learning To Let It Grow - GrepBeat

Collaborative artificial intelligence startup FedML Inc. said today it has closed on a $6 million seed funding round that will help it bring together companies and developers to train, deploy and customize machine learning models anywhere, across thousands of edge- and cloud-hosted nodes.

Todays round was led by Camford Capital and saw participation from Plug and Play Ventures, AimTop Ventures, Acequia Capital and LDV Partners.

Despite only just closing on its first round of funding, FedML has already created an open-source community, enterprise platform and various software tools that make it easier for people to collaborate on machine learning projects. They can do this by sharing data, models and compute resources, the company explained.

FedMLs mission is to create an ecosystem that will meet enterprise demands for custom AI models. It says that there are a number of businesses that want to train or fine-tune AI models on their own data so they can leverage them for more specific tasks such as business automation, customer service, product design and so on. But this data is often extremely sensitive and regulated, or else siloed, making it difficult to use cloud-based AI training systems.

To overcome this, FedML has created a federated learning platform that makes it possible for developers to collaboratively train AI models using private or siloed data at the edge, without needing to move that data anywhere else. FedML calls this approach learning without sharing. So, for example, a retail company could build models for personalized shopping recommendations without exposing a customers private data. A healthcare company would be able to build an AI model thats able to detect rare diseases by training it on scarce and extremely sensitive healthcare records that might be spread across multiple hospitals.

According to FedML co-founder and Chief Executive Salman Avestimehr, the future application of AI will depend on these kinds of collaborations. We want to create a community that trains, serves and mines the best AI models, he said. For example, we enable data owners to contribute their data to a machine learning task, and they can work with AI developers or training specialists to build a customized machine learning model, and everyone gets rewarded for their contributions.

Besides bringing the concept of federated learning to AI, FedML believes its collaborative approach will help to overcome the cost and complexity of large-scale AI development. OpenAI LP, the company that built ChatGPT, spent millions of dollars to train that model.

Of course, many companies do not have that kind of money to throw at AI training, meaning that the best models are limited to only the biggest technology firms. AI training is not only expensive, but also very complex, requiring significant expertise that not every company has. FedML reckons these challenges can be overcome with its collaborative, open-source AI development community.

We allow people to train anywhere and serve anywhere, from edge to cloud, enabling lower-cost and decentralized AI development thats accessible to everyone, said FedMLs other co-founder and Chief Technology Officer Chaoyang He.

FedMLs platform was launched in March 2022 after three years of development, and has already surpassed Google LLCs TensorFlow Federated as the most popular open-source library for federated machine learning projects. In addition, the company has created an MLOps ecosystem for training machine learning models anywhere at the edge or in the cloud. This ecosystem has more than 1,900 users, who have deployed FedML more than 3,500 edge devices and trained more than 6,500 models.

The startup has also signed 10 enterprise contracts spanning industries such as healthcare, financial services, retail, logistics, smart cities, Web3 and generative AI.

Constellation Research Inc. Vice President and Principal Analyst Andy Thuraitold SiliconANGLE that FedML has gained quite a bit of traction since its release last year, thanks to its open-source libraries and its cheaper pricing model. However, he said it has barely made a dent in terms of the full machine learning operations lifecycle. More and more, enterprises are looking towards full cycle MLOps platforms because its difficult to bring best-of-breed ML models to market without them, he explained.

That said, Thurai thinks FedML offers a lot of potential, especially if the concept of training smaller models using private datasets takes off. He said FedMLs advantage is it enables model training without needing to share data, which can be extremely useful in regulated industries and regions where data privacy is of special importance, such as the EU.

If the concept of model training at the edge using localized data takes off, then FedML can have a big impact on this, Thurai said. For now, LLMs and ChatGPT-type models are the craze, with most enterprises going for bigger and better AI models, so it will take some time to change that mindset.

Though a lot of work remains to be done, Camford Capitals partner Ali Farahanchi said he was impressed with FedMLs compelling vision and unique technology, which will enable open and collaborative AI at scale. In a world where every company needs to harness AI, we believe FedML will power both company and community innovation that democratizes AI adoption, he said.

Go here to see the original:
Collaborative machine learning startup FedML raises $6M to train ... - SiliconANGLE News

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