Daily Archives: August 20, 2022

The proliferation of 5G wireless networks will encourage communities of AI application developers to create new solutions that take advantage of 5G speed and low latency.

Cellular communications have reached the next stage of evolution, supporting real-time, machine-to-machine connectivity as well as telephone services. 5G or fifth-generation wireless technology has increased the speed and responsiveness of wireless networks so data can be transmitted at multigigabit speeds, with theoretical speeds up to 20 Gbps. In fact, 5G wireless networks exceed the speed of most wired networks. The increased performance of 5G wireless connectivity also enables complex computing applications like artificial intelligence (AI), creating new opportunities for wireless applications. Adding no-code AI development brings an even greater potential to 5G networking.

The availability of 5G high-speed wireless technology promises the expansion of technologies such as the Internet of Things (IoT), which connects devices. Using 5G wireless systems and IoT sensors, you can remotely monitor traffic conditions, monitor patients, monitor conditions to activate farm irrigation systems, and manage autonomous things like smart cars or drones. You can improve the performance of 5G networks even further with edge computing, where you place the computing resources needed to process application data closer to the location where the data is used to minimize response time.

5G wireless infrastructures can easily support AI processing. Where 5G simplifies and speeds up the integration of multiple technologies, AI gives those systems intelligence and the ability to learn from the available data. AI simulates human intelligence by applying three cognitive skills: learning which includes acquiring data and applying rules, reasoning by choosing the right data to create the desired result, and self-correction, which is fine-tuning data sorting for more accurate results.

The marriage of 5G and AI is already shaping the future of wireless ecosystems, and with no-code AI development platforms, applications that take advantage of AI and machine learning (ML) will increase. Putting AI development in the hands of IT professionals and domain experts will eliminate the need for data scientists to automate next-generation wireless applications.

See also: Manufacturing Ahead of the Curve via AI, 5G, and Edge

AI is already an integral part of cellular networking. Mobile communications services use AI to optimize network performance and reduce capital expenditures. 5G service providers use AI to improve customer service and provide personalized support. Service providers also use AI for 5G network planning, network performance management, lifecycle management, and revenue management.

AI is also having a direct impact on 5G network performance. While phones have become smarter, the core smartphone algorithms havent changed since the 1990s. As a result, 5G systems use more power and achieve lower data rates than expected. Using AI-enabled algorithms with deep learning capabilities reduces power consumption and improves performance. AI also is being used to resolve issues related to available RF frequencies for cellular signals to alleviate bandwidth overcrowding.

Low latency also makes 5G networks ideal for applications that benefit from faster response times, such as real-time video, which depends on AI to function. Using AI/ML for 5G systems also powers proactive network responses, creating dynamic cellular clusters that use learned data to improve latency, efficiency, and reliability.

While AI/ML is being used to power a new generation of 5G applications as well as the 5G infrastructure. No-code developer tools will make these AI/ML applications available to everyone.

There is an ongoing shortage of AI developers. To bridge the skills gap, no-code AI development tools are putting the power of article intelligence in the hands of IT managers and subject-matter experts. No-code platforms abstract AI complexity to make building, experimenting, and deploying AI/ML software easier. Rather than requiring data scientists with deep AI expertise, no-code AI platforms provide a drag-and-drop interface so any enterprise manager can create their own AI applications.

The proliferation of 5G wireless networks will encourage communities of AI application developers to create new solutions that take advantage of 5G speed and low latency. No-code AI development platforms will promote innovation. A growing user community and an open architecture platform will make 5G more accessible and enable new, best-in-class models and tools that can be incorporated into AI/ML workflows.

Organizations are building private 5G networks as well. Organizations with a large number of IoT sensors or endpoints benefit from private 5G because a dedicated network delivers ultra-low latency and extremely high bandwidth. 5G was initially designed to accommodate massive sensor grids for IoT applications. IoT devices and sensors generate large volumes of data, so a conventional core network or centralized cloud infrastructure cant handle the traffic. Using edge computing for low latency is ideal for AI applications that need real-time efficiency.

While 5G technology is proving ideal for real-time applications, there are still obstacles that are slowing 5G adoption. There are different flavors of 5G, each with different performance characteristics. Carriers are still building towers, power stations, data centers, sensors, and infrastructure to make 5G available to everyone in the United States, but it could take years to complete a 5G infrastructure.

In the meantime, entrepreneurs are stepping in to solve the complex problems posed by 5G networking. Innovators backed by investors and government policies are creating easy-to-use AI solutions that take advantage of the existing 5G infrastructure. The growth of no-code AI/ML development tools will lower the barriers to entry for 5G innovators and empower a new class of AI users.

Excerpt from:

Empowering the AI Enterprise in the 5G Era - RTInsights

The founder and CEO of Midjourney, David Holz, has some truly inspiring views around how AI image generation will transform the gaming industry. During the short time we spoke this week, I had to hold myself back from falling too deep into the AI rabbit hole. In the process, I discovered Holz's view on how this kind of tech will develop and how it's likely to benefit the gaming industry, as well as human creativity as a whole.

Holz believes that one day in the near future, "you'll be able to buy a console with a giant AI chip and all the games will be dreams."

It's a beautiful sentiment for sure, but it's the physics of current technology that's holding us back from exploring the full potential of AI in games. Right now these kinds of AI generators use excruciating amounts of graphical power (opens in new tab), and it's just not practical for the kind of utopian visions Holz and I have dreamed of.

He tells me that Midjourney's produces images (opens in new tab) using algorithms that "all run on the cloud, and they're running on very big GPUslike $40,000 GPU servers I think it's fair to say that it's the most compute-heavy consumer application that's ever existed."

That's a lot of energy and a great deal of money to sink into anything, but Holz truly believes in the benefits of the technology Midjourney is pioneering.

He tells me it's already being used as a way of self soothing after a traumatic event. "Some of them are actually using the AI in a purely therapeutic process. And it's hard to understand that, but you'll see weird images and you'll ask them 'why are you doing Maltese dogs in heaven?' And they'll say 'it's because my dogs just died.' And you're like 'oh my god, are you okay?'"

Of course, there's always that looming fear around AI replacing humans, but Holz has a much more positive outlook.

"We're not trying to build God, we're trying to amplify the imaginative powers of the human species," he says.

He makes it clear it's not about designing tech to replace people, it's about the "proliferation of the visual means to express yourself. It just means that people will become more visual in our culture, and more appreciative of those kinds of things. And there'll be more opportunity around that than there was before."

The barriers between consuming something and creating something fall away

I'm much in the same mind, and having come from a game art and design background I can certainly see its potential in idea generation for concept artists.

"Before you see video games being generated on the fly, you're gonna see the technologies being used for every step of the asset generation pipeline, to increase the creativity of the content, the quality of the content, and the amount of the content," says Holz. "...you're gonna have game studios using AI to help bake out lots of assets, textures, terrain, layouts and characters. Even if it takes ten minutes to make a high quality character, that's still much faster than it would take during the normal production process.

"One would hope that in ten years time there's no longer static content because everything is generated on the fly. So in theory, the barriers between consuming something and creating something fall away, and it becomes like liquid imagination flowing around the room.

"Everything between now and then is a combination of increasing the quality, being able to do things like 3D, making things faster, making things higher resolution, and having smaller and smaller chips doing more and more stuff."

So it seems we just have to wait for the technology to catch up. And it is catching up, fast. There are, albeit less powerful, AI image generators that run on consumer hardware (opens in new tab), and it's only a matter of time before these algorithms are even more efficient and involved, so we can get down to generating entire triple A games as we play.

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One day consoles will have a 'giant AI chip and all the games will be dreams' - PC Gamer

Conservation apps have emerged in recent years as an efficient and cost-effective way to get citizens to monitor and document wildlife across the world. But an Australian initiative is going one step further.

In a bid to detect and save the countrys dwindling koala (Phascolarctos cinereus) population, Conservation AI Hub has, since the beginning of this year, been training volunteers in the state of Queensland to use infrared drones. The goal: to find koalas that are usually found curled high up in the trees. The initiative by the Queensland University of Technology (QUT) began earlier, in August 2021, with a small team operating the drones. They then analyzed the images collected using artificial intelligence algorithms. But as the project scaled up, the need to rope in volunteers became apparent.

Its an urgent mission. The deadly bushfires that blazed through large swathes of Australia in 2019 and 2020 decimated the countrys already vulnerable koala population: a 2021 report by the Australian Koala Foundation found that the country lost 30% of its koala population in the past three years. Earlier this year, the Australian government declared the species endangered in much of eastern Australia.

The situation is pretty dire, Grant Hamilton, director of Conservation AI Hub, tells Mongabay in a video interview. In the face of the climate and biodiversity crises, he says, its more essential than ever to get citizens involved in conservation efforts. And getting them access to and acquainted with technology is a good place to start.

Mongabays Abhishyant Kidangoor spoke with Hamilton on the work done by Conservation AI Hub, the role of conservation technology in a climate-ravaged future, and why its up to the common citizen to stand up for the planets biodiversity. The following interview has been lightly edited for style and clarity.

Mongabay: To start with, could you tell me where your interest in wildlife conservation stemmed from?

Grant Hamilton: I grew up in a small town in northern New South Wales in Australia, and I was always out in nature. At the time that I grew up, it was very much a conservation-minded kind of area. There were some particularly large protests when I was young that had to do with logging. And that really made an impact on me. Frankly, for a lot of people from my generation, there is a strong drive toward conservation that hasnt necessarily been translated through to the political will globally. But there are still a lot of people who are very devoted to it.

Mongabay: How did technology come to play a part in your work?

Grant Hamilton: I am a quantitative ecologist which means that I often create models to understand ecology. In the case of threatened species, one of the important things is simply finding where they are, and counting how many of them there are. Fundamentally, if we cant estimate how many animals there are of a particular species, then we dont know if the numbers are going down or up. We dont know if the time, energy and money we are investing into managing them is effective or not. And its surprisingly hard to find things. For koalas, in particular, which is where I did a reasonable amount of this work, they are really difficult to see. They are up in a canopy. Humans are down on the ground, looking through binoculars. Its a real challenge. There are some amazing people who do it really quite well. But even when they are doing it well, they are only getting to spotting two koalas for every three that are out there. And thats quite a large error rate. So part of my interest in technology was about how we think about doing things at a large scale and doing it more efficiently.

Mongabay: How do you think technology is faring when it comes to wildlife conservation?

Grant Hamilton: I think its on the increase. There is a recognition of the advantages. I think people are increasingly recognizing that the scale of the problem is not going to be solved simply by traditional tools. While I will never say that these technologies are the only solution, it would not be smart to ignore them. And it makes a lot of sense to try and expand them to find the areas where they are most practical, most efficient and can save us money.

Mongabay: Could you tell me how Conservation AI Hub works and how your team uses technology on the ground?

Grant Hamilton: Fundamentally, it is a data portal connected to a set of AI algorithms. The partners that we are working with will collect data in an appropriate way using appropriate technology. So that might be using camera traps, or it might be using drones, and they will feed that through to us. Then, we either use the algorithms that we have already developed, or we develop new algorithms, and work with those partners to get that data back to them. For example, after the Black Summer bushfires in Australia in 2019 and 2020, we did some work in Kangaroo Island in South Australia, where we were helping them to use drones to be able to detect koalas. Increasingly, we are also using camera traps.

Mongabay: What is the workflow after the data is collected?

Grant Hamilton: What happens next is that we run the data through an artificial intelligence algorithm to look for koalas. It is possible to get people to look through thermal imagery to look for koalas, but the challenge is that we might be looking at over 50 or 60 or 80 hectares [120-200 acres] at a single time and thats an enormous amount of thermal imagery data. So we use artificial intelligence to scan through all the data really quickly to be able to find the koalas. We then have a quality assurance phase to make sure that we are not getting too many false positives [koala sightings that arent actually koalas]. We then share the analyzed data so that it can be for everybody to benefit from, but only if it is appropriate.

Mongabay: What are the cases where sharing the data is not appropriate?

Grant Hamilton: There might be areas where perhaps Traditional Owners [Indigenous Australians with a spiritual or cultural connection to the land] dont consider that its appropriate for us to share data. Sometimes with threatened species, you dont actually want to let people know where those threatened species are. So, in the near term, there might be an embargo on such data, but the intention is always going to be to share that data.

Mongabay: Could you give me a ground-level example of a project Conservation AI Hub is working on currently?

Grant Hamilton: At the moment, what I am really excited about is one of the projects that the Conservation AI Hub is involved with, which is called the WildSeek. We are working together with Landcare Australia [a grassroots environmental organization], and we are training groups of volunteers to fly drones to collect data, which they send through to my research group at QUT. We then use artificial intelligence to detect koalas, and we deliver the detections back to them. As far as I know, this is a world first, this kind of training and application. The powerful and exciting thing about this is its the people who manage the land who are actually collecting the data, and then receiving the data back to help them manage better. We are aiming to have five groups on board within the next couple of months, but I see no reason why within two or three years, we may not have 20. Essentially if you are looking at how to scale conservation, I believe this to be a very effective model.

Mongabay: What has the impact on the ground been like?

Grant Hamilton: Its relatively new. We launched at the beginning of the year. For now, the model is the exciting bit the model of having volunteers that we are bringing on board to help with this. Often theres a disconnect between the people who monitor and the people who manage. But what we are doing now is amped-up citizen science which would enable folks on the ground to have better data to be able to manage better. We have one group that we have been working with in Noosa, which is a coastal town in southeast Queensland [state], where there are some really amazing koala habitats. But some of it is quite dense, and it can be quite difficult to get to. So what we have been doing is flying drones on these sites regularly to detect koalas across very large areas where it would have been really challenging for people to be able to do that. The other incredibly exciting thing is, as replanting is going on in that area, we are planning to continually resurvey to determine if koalas are moving into these replanted areas.

Mongabay: Before WildSeek was launched, how was the data collection done?

Grant Hamilton: Earlier, we would be the ones to go out and collect the data. We would send out drone operators from QUT to collect the data, and we would do essentially everything in-house. But it soon became apparent that if you want to scale this up in many countries, sending out a single team of drone operators wasnt enough. It is a very good proof of concept, but its not effective if you want to make broad-scale ground impacts. So thats how the idea developed to start working with volunteer groups, and to buy the equipment for them and train them so that we can then process the data for them.

Mongabay: What are the challenges you have faced in terms of using technology?

Grant Hamilton: There are a set of hurdles which are going to be issues with any monitoring method you use. That has to do with planning how you fly a drone. Are drones appropriate for the species that you are looking for? Are camera traps appropriate for the species that you are looking for? To fly drones, for example, if you are flying a reasonably sized drone, you need appropriate training. Its large, and you need to understand what the laws are in your area around flying drones. Being able to fly the drone is not enough. You need to be able to fly it in the appropriate way with an appropriate sensor. And theres a whole set of constraints around this: the height, the speed, and the size of the organism that youre looking for. But we have managed to work this out over a number of years for different kinds of species. There is a considerable technical challenge in ingesting data across networks from a long way away. You might get interruptions and you need to make sure that that data still gets there whole. In each step along the way, there are going to be site-specific hurdles. There will always be issues, but thats part of the fun. And hopefully, we will resolve them in a way that contributes better to conservation.

Mongabay: If you were to chat with someone who develops technology for conservation, what would you tell them are the technological gaps you face?

Grant Hamilton: Drones are just a platform to carry the real thing that we are interested in, and thats the sensor that collects the data. There are some really amazing sensors currently, but they are very expensive, and some of them are quite heavy that you might need to fly them in a helicopter, rather than on a drone. So what I hope and would expect to see coming along is more high-resolution and more lightweight sensors. But low cost is going to be a very important thing, particularly if we are talking about empowering citizens to go and collect data. Finding ways to enable people to engage with this, and really to democratize conservation is really important. Partly, you are always going to have to work with folks like us, because the development of quantitative analytical tools and machine-learning tools takes a fair bit of training. But the idea is about data collection, and using people who are out there. There are a lot of people passionate about conservation, and they want to get involved. So marrying the technology with those people who want to help will be an incredibly important thing for the folks who make this technology. If they can step up in that way, that would be a huge boost.

Mongabay: Where do you see Conservation AI Hub going from here?

Grant Hamilton: We want to help to save the world. People are focusing, quite rightly, on the fact that we are facing a climate crisis. But we are also facing a biodiversity crisis. What people dont often recognize is that when biodiversity goes, so do we. I would argue that ecosystems have an innate right to persist. It shouldnt all be about the utility for human beings.

What we are trying to do is to get that first step of monitoring, so that we can point out where the problems are and determine if the management taking place is being done effectively. We want to be able to hold up those areas where things are being done badly. Its going to be up to us common citizens to go out there and stand up for biodiversity.

Mongabay: In a rapidly heating world which is also facing a biodiversity crisis, what role do you think technology can play?

Grant Hamilton: There has been a set of skills around wildlife observation and conservation. Theres a learning curve, which is potentially hard for people. If we can lower that bar to enable them to collect data in their surrounding environment, in the place where they are invested in, the area where they live, if we can lower that bar to allow them to help, that has to be a good thing. That is partly what conservation technology is going to do. It is going to allow us to record and see where things are going wrong. Perhaps occasionally, we might get wins and we might see things that are going well and we might learn from those successes. But certainly, if we allow people to engage with conservation technology, help them to understand what it means that awareness, I believe, has to be a good thing for biodiversity. Especially if it translates to the political process and helps to put pressure on politicians, organizations and companies to do better.

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'Amped-up citizen science' to save the world: Q&A with Conservation AI Hub's Grant Hamilton - Mongabay.com

Making sense of AI decisions is important to researchers, decision-makers, and the wider public. Fortunately, there are methods available to ensure we know more.

Deep-learning models, of the type that are used by leading-edge AI corporations and academics, have become so complex that even the researchers that built the models struggle to understand decisions being made.

This was shown most clearly to a wide audience during DeepMinds AlphaGo tournament, in which data scientists and pro-Go players were regularly bamboozled by the AIs decision-making during the game, as it made unorthodox plays which were not considered the strongest move.

SEE ALSO: Artificial Intelligence More Accepted Post-Covid According to Study

In an attempt to better understand the models they build, AI researchers have developed three main explanation methods. These are local explanation methods, which explain one specific decision, rather than the decision making for an entire model, which can be challenging given the scale.

Yilun Zhou, a graduate student in the Interactive Robotics Group of the Computer Science and Artificial Intelligence Laboratory (CSAIL), discussed these methods in a MIT News article.

Feature attribution

With feature attribution, an AI model will identify which parts of an input were important to a specific decision. In the case of an x-ray, researchers can see a heatmap or the individual pixels that the model perceived as most important to making its decision.

Using this feature attribution explanation, you can check to see whether a spurious correlation is a concern. For instance, it will show if the pixels in a watermark are highlighted or if the pixels in an actual tumor are highlighted, said Zhou.

Counterfactual explanation

When coming to a decision, the human on the other side may be confused as to why an AI has decided one way or the other. As AI is being deployed in high-stakes environments, such as in prisons, insurance, or mortgages, knowing why an AI rejected an offer or appeal should help them attain approval the next time they apply.

The good thing about the [counterfactual] explanation method is it tells you exactly how you need to change the input to flip the decision, which could have practical usage. For someone who is applying for a mortgage and didnt get it, this explanation would tell them what they need to do to achieve their desired outcome, said Zhou.

Sample importance

Sample importance explanation requires access to the underlying data behind the model. If a researcher notices what they perceive to be an error, they can run a sample importance explanation to see if the AI was fed data that it couldnt compute, which led to an error in judgment.

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Three Methods Researchers Use To Understand AI Decisions - RTInsights

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The last two years saw a major rise in home deliveries. Around 60% of the global population will live in cities by 2030 and last-mile deliveries are expected to grow further by 78%, according to the World Economic Forum.

Currently, the global last-mile delivery market size stands at $130 billion, with 4160 parcels shipped every second. The number comes to about 150 billion annually a number that is always rising owing to ecommerce growth and changing consumer behavior according to Pitney Bowes Parcel Shipping Index. All major organizations like McDonalds, Starbucks, Walmart and UPS are investing heavily to focus on home deliveries and provide the best digital delivery experience.

Today, its possible to order everything online and companies have to be prepared to fulfill these customer demands. Both business-to-consumer (B2C) and business-to-business (B2B) shipments have grown significantly, and customers expect the best delivery experience.

LogiNext, a New York-based technology logistics automation company for large enterprises, claims its proprietary artificial intelligence (AI)-powered logistics management solutions provide a software-as-a-service (SaaS)-based solution to help enterprises digitize, optimize and automate end-to-end logistics operations. With predictive ETAs, the platform kicks into action even before a consumer places an order online and helps a brand orchestrate deliveries until it reaches the doorstep (including handling returns).

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Dhruvil Sanghvi, CEO at LogiNext, told VentureBeat that LogiNexts platform has tracked more than 13 billion location data points, supporting and processing more than 500 million orders annually. According to Sanghvi, LogiNexts platform enables clients to deliver faster, more efficiently offering clear-cut insights into the best possible routes for drivers and giving real-time visibility over the entire logistical operations.

Sanghvi noted that traditional brands aiming to offer home deliveries like digital natives have a few hurdles to cross.

1. Lack of digitization: Several large organizations have logistics operations heavily dependent on manual tasks. Digitizing processes from inventory management to collecting e-signatures to analytics and reporting is critical for smooth home delivery operations.

2. Legacy technology systems: While many companies use some technology, they are often legacy on-premise systems that are expensive to install and maintain. New age SaaS platforms can easily replace legacy systems and give operations managers a great digital experience to manage deliveries.

3. Driver management: There are orders coming in from several channels, and organizations rely on in-house drivers or a third-party fleet. Managing all of this on Excel sheets is highly inefficient and leads to increasing delivery costs.

Sanghvi said what LogiNext does is heavy on artificial intelligence (AI). The SaaS platform helps enterprises deliver in a smarter manner and, hence, faster.

For our algorithm to assign orders and figure out the best routes automatically, it must consider things like traffic patterns, weather conditions, transport regulations, peak hours, customer preference and several other variables, he said.

To help enterprises optimize logistics, LogiNexts AI and ML capabilities are helpful for route optimization, capacity utilization, dynamic order allocation and visibility over the entire supply chain.

One of the foremost advantages of using LogiNext is digitization. There are still a lot of manual processes in the logistics industry which first need to get digitized and then move towards automation. The more historic and current data we have, the more were able to use AI to optimize logistics delivery, he said.

According to Sanghvi, home deliveries for food and beverage (F&B), retail, ecommerce and courier companies are the largest segments adopting logistics automation to make last mile deliveries more efficient.

One of the major challenges in the enterprise ecosystem today is unstructured data. Sanghvi said many supply chain companies still handle a lot of their processes manually, collecting data in Excel sheets making it difficult to contextualize data. He said LogiNext helps to digitize companies entire systems to ensure they are collecting, labeling and using data in the best possible way.

To achieve true success in their digital transformation journey, Sanghvi said organizations must prioritize automation, making processes more efficient. Logistics has a huge scope for this.

Digital transformation and automating processes are two huge impact areas we help companies with, said Sanghvi who highlighted how one of LogiNexts major customers in North America reduced the order life cycle of a product from 12 hours to 2.2 hours.

While Sanghvi considers the likes of Project 44 and Bringg as LogiNexts major competitors, he said LogiNext has a differentiator in the fact that it is an end-to-end platform with elements of visibility and routing. With 100+ out-of-the-box API integrations, companies can plug and play to go live quickly, as they dont need to revamp any of their existing technology. The LogiNext platform sits smoothly over any existing technology through the APIs and webhooks.

He said another differentiator is LogiNexts ability to provide end users with a superior customer experience where the end customer is communicated ETAs in real time with contextual alerts & notifications.

A lot of our competitors often focus on one particular thing either they focus only on visibility or only on routing. We focus on all aspects of the delivery process, from the first mile to the last mile. So, you can think of us more as horizontal players rather than vertical players, said Sanghvi.

Founded in 2014 by CEO Dhruvil Sanghvi, LogiNext is headquartered in New York, with the companys founding team and business development teams in New Jersey. While LogiNexts engineering and research teams are based in Mumbai, the company has small offices in Jakarta, Singapore and Dubai.

Growing at 120% year-over-year and with a valuation of between $300-$500 million, LogiNext says it aims to go for its initial public offering (IPO) in the next five to seven years, building a global company with cutting-edge tech for logistics, especially in the home delivery and last-mile segment.

LogiNext has a current headcount of about 200 and has raised $49.5 million in total funding to date from investors like Tiger Global, Steadview Capital and Alibaba group of companies. The company currently serves 200+ enterprise clients in over 50 countries globally, including companies like McDonalds, KFC, BurgerKing, Decathlon, SingaporePost, among others.

Sanghvi said LogiNext provides its software to companies who want to compete with Amazon on their delivery experience. The company has been named a sample vendor in Gartners Hype Cycle on many occasions, and also received Frost and Sullivans 2022 Customer Value Leadership Award.

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AI-powered delivery automation streamlined for the enterprise - VentureBeat

Why it matters: Researchers continue to find new ways to leverage artificial intelligence and machine learning capabilities as the technologies evolve. Earlier this week, Google scientists announced the creation of Transframer, a new framework with the ability to generate short videos based on singular image inputs. The new technology could someday augment traditional rendering solutions, allowing developers to create virtual environments based on machine learning capabilities.

The new framework's name (and, in some ways, concept) are a nod to another AI-based model known as Transformer. Originally introduced in 2017, Transformer is a novel neural network architecture with the ability to generate text by modeling and comparing other words in a sentence. The model has since been included in standard deep learning frameworks such as TensorFlow and PyTorch.

Just as Transformer uses language to predict potential outputs, Transframer uses context images with similar attributes in conjunction with a query annotation to create short videos. The resulting videos move around the target image and visualize accurate perspectives despite having not provided any geometric data in the original image inputs.

The new technology, demonstrated using Google's DeepMind AI platform, functions by analyzing a single photo context image to obtain key pieces of image data and generate additional images. During this analysis, the system identifies the picture's framing, which in turn helps the system to predict the picture's surroundings.

The context images are then used to further predict how an image would appear from different angles. The prediction models the probability of additional image frames based on the data, annotations, and any other information available from the context frames.

The framework marks a huge step in video technology by providing the ability to generate reasonably accurate video based on a very limited set of data. Transframer tasks have also shown extremely promising results on other video-related tasks and benchmarks such as semantic segmentation, image classification, and optical flow predictions.

The implications for video-based industries, such as game development, could be potentially huge. Current game development environments rely on core rendering techniques such as shading, texture mapping, depth of field, and ray tracing. Technologies such as Transframer have the potential to offer developers a completely new development path by using AI and machine learning to build their environments while reducing the time, resources, and effort needed to create them.

Image credit: DeepMind

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Researchers announce new AI-based technology that can create short videos based on single images - TechSpot

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I have been thinking about death lately. Not a lot a little. Possibly because I recently had a month-long bout of Covid-19. And, I read a recent story about the passing of the actor Ed Asner, famous for his role as Lou Grant in The Mary Tyler Moore Show. More specifically, the story of his memorial service where mourners were invited to talk with Asner through an interactive display that featured video and audio that he recorded before he died. The experience was created by StoryFile, a company with the mission to make AI more human. According to the company, their proprietary technology and AI can match pre-recorded answers with future questions, allowing for a real-time yet asynchronous conversation.

In other words, it feels like a Zoom conversation with a living person.

Even though the deceased is materially gone, their legacy appears to live on, allowing loved ones, friends, and other interested parties to interact with them. The company has also developed these experiences for others, including the still very much alive William Shatner. Through this interactive experience, I asked Shatner if he had any regrets. He then spoke at length about personal responsibility, eventually coming back to the question (in Shatner-like style). The answer, by the way, is no.

There are other companies developing similar technology such as HereAfter AI. Using conversational AI, the company aspires to reinvent remembrance, offering its clients digital immortality. This technology evolved from an earlier chatbot developed by a son hoping to capture his dying fathers memories.

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MetaBeat will bring together thought leaders to give guidance on how metaverse technology will transform the way all industries communicate and do business on October 4 in San Francisco, CA.

It is easy to see the allure of this possibility. My father passed away ten years ago, shortly before this technology was available. While he did write a short book containing some of his memories, I wish I had hours of video and audio of him talking about his life that I could query and both see and hear the responses in his own voice. Then, in some sense, he would seem to still be alive.

This desire to bring our deceased loved ones back to life is understandable as a motivation and helps to explain these companies and their potential. Another company is ETER9, a social network set up by Portuguese developer Henrique Jorge. He shared the multi-generational appeal of these capabilities: Some years from now, your great-grandchildren will be able to talk with you even if they didnt have the chance to know you in person.

In Be Right Back, an episode from the Netflix show Black Mirror, a woman loses her boyfriend in a car accident and develops an attachment to an AI-powered synthetic recreation. This spoke to the human need for love and connection.

In much the same way, a young man named Joshua who lost his girlfriend Jessica to an autoimmune disease recreated her presence through a text-based bot developed by Project December using OpenAIs GPT-3 large language transformer. He provided snippets of information about Jessicas interests and their conversations, as well as some of her social media posts.

The experience for Joshua was vivid and moving, especially since the bot said exactly the sort of thing the real Jessica would have said (in his estimation). Moreover, interacting with the bot enabled him to achieve a kind of catharsis and closure after years of grief. This is more remarkable since he had tried therapy and dating without significant results; he still could not move on. In discussing these bot capabilities, Project December developer Jason Rohrer said: It may not be the first intelligent machine. But it kind of feels like its the first machine with a soul.

It likely will not be the last. For example, Microsoft announced in 2021 that it had secureda patent for software that could reincarnate people as a chatbot, opening the door to even wider use of AI to bring the dead back to life.

Weve got to verify it legally

To see if she is morally, ethically

Spiritually, physically

Positively, absolutely

Undeniably and reliably dead!

In the novel Fall; or, Dodge in Hell, author Neal Stephenson imagines a digital afterlife known as Bitworld contrasting the here and now of Meatworld. In the novel, the tech industry eventually develops the ability to map Dodges brain through precise scanning of the one hundred billion neurons and seven hundred trillion synaptic connections humans have, upload this connectome to the cloud and somehow turn it on in a digital realm. Once Dodges digital consciousness is up-and-running, thousands of other souls who have died in Meatworld join the evolving AI-createdlandscape that becomes Bitworld. Collectively, they develop a digital world in which these souls have what appears as consciousness and a form of tech-fueled immortality, a digital reincarnation.

Just as the technology did not exist ten years ago to create bots that virtually maintain the memories and to a degree the presence of the deceased, today the technology does not exist to create a human connectome or Bitworld. According to Louis Rosenberg of Unanimous A.I.: This is a wildly challenging task but is theoretically feasible.

And people are working on these technologies now through the ongoing advances in AI, neurobiology, supercomputing, and quantum computing.

Neuralink, a company founded by Elon Musk focused on brain-machine interfaces, is working on aspects of mind-uploading. Some number of wealthy people, including tech entrepreneur Peter Thiel, have reportedly arranged to have their bodies preserved after death until such time as the requisite technology exists. Alcor is one such organization offering this preservation service. As futurist and former Alcor CEO Max Moore said: Our view is that when we call someone dead its a bit of an arbitrary line. In fact, they are in need of a rescue.

The mind-uploading concept is also explored in the Amazon series Upload, in which a mans memories and personality are uploaded into a lookalike avatar. This avatar resides in what passes for an eternal digital afterlife in a place known as Lakeview.In response, an Engadget article asked: Even if some technology could take all of the matter in your brain and upload it to the cloud, is the resulting consciousness stillyou?

This is one of many questions, but ultimately may be the most relevant and one that likely cannot be answered until the technology exists.

When might that be? In the same Engadget article, Upload showrunner Greg Daniels implies that the ability to upload consciousness is all about information in the brain, noting that it is a finite amount, albeit a large amount. And if you had a large enough computer, and a quick enough way to scan it, you ought to be able to measure everything, all the information thats in someones brain.

The ethical questions this raises could rival the connectome in number and will become critical much sooner than we think.

Although in the end, I would just like to talk with my dad again.

Gary Grossman is the senior VP of technology practice atEdelmanand global lead of the Edelman AI Center of Excellence.

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Death, resurrection and digital immortality in an AI world - VentureBeat

image:Quentin Vanhaelen, PhD, of Insilico Medicine will present on the Company's AI-powered target discovery platform PandaOmics at the 9th annual Aging Research and Drug Discovery conference. view more

Credit: c/o Insilico Medicine

The 9th annual Aging Research and Drug Discovery Conference, happening Aug. 29-Sept. 2 at the University of Copenhagen, will feature researcher Quentin Vanhaelen, PhD from Insilico Medicine discussing the Companys artificial intelligence target discovery platform, PandaOmics. The platform has been validated through numerous drugs in development, including thefirstAI-discovered and AI-designed drug for idiopathic pulmonary fibrosis,currently in Phase I trials. Vanhaelen is one of over 70academic and industry leaders in longevity speaking at the event, and will describe theprogress that PandaOmics has made since Insilico launched it in 2020.

PandaOmics uses aging as an important biomarker, sifting through trillions of data points from clinical trials, research grants, and omics data samples, including transciptomics, genomics, epigenomics, and proteomics. The system identifies where aging and disease intersect and how aging contributes to poorer health. Researchers have used PandaOmics to predict ninemolecular targets for new drugs that can combat aging as well as aging-associated diseases including Alzheimers, Parkinsons, cirrhosis, and rheumatoid arthritis.

Vanhaelen will discuss the possibilities of developing dual-purpose drugs using AI, and talk about how the PandaOmics platform works using data to discover where age-associated and non-age-associated diseases overlap, and evaluating targets' therapeuticpotential by ranking them for druggability and safety. And Vanhaelen will talk about the benefits of an AI approach, which allows scientists to make these discoveries in record time. The nine novel dual-purpose aging and disease targets were discovered and published in less than two months.

Identifying ways to identify and treat aging is one of the many topics being explored relative to expanding human lifespan and healthspan at the ARDD. The conference, founded by Insilico Medicine founder and CEO Alex Zhavoronkov, PhD, brings together experts in longevity to share breakthroughs, collaborate, and advance aging research.

This conference was designed to create the worlds first platform for the pharmaceutical industry to actively engage in and incorporate the latest discoveries in credible aging research into every aspect of their internal R&D strategy, says Zhavoronkov.

Vanhaelen has been working with Insilico since 2016 and holds a PhD in theoretical physics from the University of Brussels. He founded a consultancy company called Insilicoscreen on the analysis of signaling pathways dynamics that was acquired by Insilico Medicine and his research interests includetheories of aging, signaling pathways activation, modeling of dynamical systems, and applications of deep learning techniques for drug discovery.

He will present on PandaOmics on September 2, 7:40-8pm EST.

About the ARDD

The 9th annual Aging Research and Drug Discovery (ARDD) conference brings together leading academic and industry speakers in aging research with prominent startups, venture capitalists, and editors of industry journals. The event will be held virtually and in person at the University of Copenhagen Aug. 29-Sept. 2.

Details and registration: http://www.agingpharma.org/

About Insilico Medicine

Insilico Medicine, a clinical stage end-to-end artificial intelligence (AI)-driven drug discovery company, is connecting biology, chemistry, and clinical trials analysis using next-generation AI systems. The company has developed AI platforms that utilize deep generative models, reinforcement learning, transformers, and other modern machine learning techniques to discover novel targets and to design novel molecular structures with desired properties. Insilico Medicine is delivering breakthrough solutions to discover and develop innovative drugs for cancer, fibrosis, immunity, central nervous system (CNS) diseases and aging-related diseases.

For more information, visit http://www.insilico.com

For media inquiries, contact media@insilicomedicine.com

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Insilico Medicine presents on AI for drug discovery at 9th Annual Aging Research and Drug Discovery Conference - EurekAlert

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One of the major events this week has been inflation. Wait, dont go to sleep yet, we know this is hardly the first time youre hearing about inflation lately, but this time its for a different reason.

The figures for July just came out, and prices stayed exactly flat for the month of July. Thats right, for the first time in a while, weve not seen a new inflation record with the release of new data. Its not cause to break out the champagne just yet, with the 12 month figure still reaching 8.5%, but its a significant improvement from the previous month of 9.1%.

Not only did prices remain steady throughout the month, but in many cases prices actually fell. Airline tickets, clothing and (shockingly) gas prices all fell in the month of July.

In fact, much of the travel sector saw prices come off. Airfares dropped 8% over the month, the cost for a rental car dropped almost 10% and the average hotel price softened by 2.7%. After hitting a high of $5 a gallon on average, gas prices have fallen significantly and are down almost 20% to $4.01 as of Wednesday.

It wasnt all good news, with grocery prices still rising 1.3% last month and restaurants and rent both up 0.7% for the month.

Right now we cant draw too many conclusions from just a single month. It may just be a momentary respite from the relentless rising prices, or it may be a sign that the trend is starting to turn. For now, analysts will be watching upcoming economic data with interest to make a call on which of these scenarios is most likely.

The past few weeks have seen a turnaround in the fortunes of U.S. companies, with the S&P 500 up almost 15% since June 16th. The economic data that has been coming out has been better than expected in many cases, and earnings season has been a pleasant surprise for many analysts.

Overall this has created some positive movement in the U.S. stock market, but so far its not certain whether this is the beginning of a new trend or a bear bounce that will reverse shortly.

The news on inflation will be having a positive impact on sentiment, as it has been one of the overarching factors in the pessimism of recent months. At a more specific level, were seeing prices come down in some of the most important areas of the economy, in particular the price of crude oil.

Given how widely used oil is, being used in everything from plastics to transportation, a softening in the price has the potential to provide knock-on benefits to the wider economy.

Job figures released last week were also very well-received, with 528,000 new jobs being added in July. This figure was a big surprise with many analysts expecting less than 400,000 new jobs for the month.

None of this means that its smooth sailing from here on, but it gives some hope that a market turnaround could be getting closer.

The U.S. market has been beat down pretty hard so far this year. Tech stocks in particular have taken a hammering, after two years of frankly phenomenal performance. The fall has been so dramatic, that were of the opinion that its gone too far.

The U.S. stock market makes up almost 60% of the total global market cap, and yet in 2022 the S&P 500 has performed worse than many other global markets. This doesnt necessarily make sense, but that also means that its a situation that can potentially be used to your advantage.

Because of this potential mispricing, weve created the U.S. Outperformance Kit which uses a pair trade to go long on the U.S. stock market and short on the rest of the developed world. The short position includes countries like France and the UK, whos stock markets have both performed better than the U.S. so far this year.

We use AI to rebalance these positions on a weekly basis to seek the optimal risk adjusted return potential. This is a Limited Edition Kit, and with the recent turnaround in the U.S. the opportunity might not last for too much longer.

Here are some of the best ideas our AI systems are recommending for the next week and month.

Valhi Inc (VHI) The diversified holding company is our Top Buys for next week with an A rating in Quality Value and a B in Growth and Low Momentum Volatility. Revenue was up 25.7% year on year to the end of June.

Sidus Space Inc (SIDU) Aerospace and defense company Sidus Space is a Top Short for next week with our AI rating them an F in our Low Momentum Volatility and Quality Value factors. The stock has fallen 72.81% over the last 12 months.

Tronox Holdings Plc (TROX) Titanium products manufacturer Tronox Holdings is a Top Buy for next month with a B in our Quality Value, Technicals and Low Momentum Volatility factors. Earnings per share have grown 9.77% over the past 12 months.

Gamestop (GME) Everyones favorite meme stock is our Top Short for next month and our AI rates it as an F in Quality Value and Growth and a C in Low Momentum Volatility. Earnings per share are down 11.01% over the past 12 months.

Our AIs Top ETF trade for the next month is to invest in oil and gas and retail, while shorting the Asia Pacific region and floating rate bonds. Top Buys are the SPDR S&P Oil & Gas Equipment & Services ETF, SPDR S&P Retail ETF and the ProShares Ultra Bloomberg Crude Oil. Top Shorts are the Vanguard FTSE Pacific ETF and the iShares Floating Rate Bond ETF.

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Inflation Flat For The First Time In Decades: Forbes AI Newsletter - August 13th - Forbes