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"The Intersection of Sustainability and Technology: Costas Polycarpou's Innovative AI Solution"

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"The Intersection of Sustainability and Technology: Costas Polycarpou's Innovative AI Solution"

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AI may be used in a variety of settings and economic sectors to help manage environmental effects and climate change. Precision agriculture, sustainable supply chains, environmental monitoring and compliance, and improved weather and catastrophe prediction and response are a few examples of applications. Modeling the economic effect of AI's use to manage the environment across four sectors-agriculture, water, energy, and transportation-research by PwC UK was commissioned by Microsoft.

As Costas Polycarpou says, the technology is already in use to create greener smart cities in China, monitor deforestation in the Amazon, and deliver natural catastrophe alerts to Japan.

While suggesting how we use AI applications to optimize the deployment of renewable energy sources Costas Polycarpou says, we can supply solar and wind energy into the electrical grid as needed, increase power storage, and design more energy-efficient structures.

On a lesser scale, it may assist families in reducing their energy use by automatically turning off lights when not in use or rerouting electricity from electric cars to the grid to fulfil expected demand.

AI has the potential to generate 38.2 million net new employment worldwide, delivering more skilled vocations as part of this transition, at the same time as productivity gains.

According to Massachusetts Institute of Technology research, the data storage and processing required to completely train a complex algorithm can use a significant amount of energy, producing up to 626,000 pounds (284,000 kg) of carbon dioxide.

"AI has the ability to both advance and undermine efforts to combat global climate issues, we have to use in a certain direction to make much of it," says Costas Polycarpou. Adding to his words, Polycarpou says the way climate issues disrupt our daily lives, we don't have much time to combat it if no implementation is done now.

According to the International Energy Agency, data centers now consume 1% or less of the world's power to process and store data from online activities like sending emails and streaming films. Costas Polycarpou also urges, "It is far from adequate to rely on technology to fix the issue in the future and, in doing so, to ease our consciences in the short to medium term".

Furthermore, according to some projections, the global energy requirement for computers will reach 8% by 2030, which raises concerns that additional fossil fuels would need to be used as a result.

In the end, how AI is governed internationally may determine how it is used to combat climate change. The kind of technology we are now employing makes privacy concerns quite specialized. Costas Polycarpou indicates that a remedy is about to be offered. The trade-off between energy usage and privacy is made easier if more algorithms are added that rely less heavily on user data. In the near future, these algorithms would be "as efficient" as those already in use.

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Costas Polycarpou Braces AI usage to aid daunting climate changes

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Synopsis: Costas Polycarpou asserts that with the aid of AI progress, we can create a sustainable and healthy future. Artificial intelligence (AI)...

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Researchers at Stanford have developed an Artificial Intelligence (AI) model,’ StockBot’, which uses LSTMs to predict stock prices with gains higher than the most aggressive ETFs

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Researchers at Stanford have developed an Artificial Intelligence (AI) model,’ StockBot’, which uses LSTMs to predict stock prices with gains higher than the most aggressive ETFs

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Stock market behavior forecasting is a crucial undertaking that requires careful attention since, with the right choices, a successful prediction of stock prices might result in attractive gains. Due to non-stationary, noisy, inter-dependence, and chaotic data, stock market forecasting is a significant challenge, making it difficult for investors to spend their money in a way that would result in profits. Given the importance of this area, machine learning experts have proposed several models that aim to predict the future value of stock market groups.

The earlier works used traditional machine learning techniques such as support vector regression, random forests, and the bayesian model. More recently, researchers have turned to deep learning models. Deep neural networks such as LSTM and encoder-decoder are increasingly used to perform the task of stock market prediction since they are more efficient in facing the time-series nature of the data.

StockBot, a new approach proposed by researchers from Stanford University, was introduced to help investors make a daily decision: sell or buy. It is a generalizable price predicting model based on stacked LSTM aiming to predict stock prices for new stocks that do not have sufficient historical data.

Generally, LSTM-based prediction models are trained on the price of a single stock and can perform only the inference using the parameters learned on the same stock. Therefore, the authors proposed to train the network specifically to an industry type such as "energy" or "finance." Concretely, past and future prices from multiple tickers in the same industry are combined to create a mixed training and/or test set. In this way, the model can operate in two modes. Although the training step is made using the combined set, the prediction step can be done for all the tickers or just for a single one which is very useful for performing a more robust prediction for stocks with insufficient historical data. In addition, a bot is used to perform the buy or sell operations at the time of closing every day in order to maximize gains. The decision is made by use of the predictions of the stock prices analytically without any training phase. The algorithm followed by the bot is as follows:

1) Calculate the δi changes given by δi = sign(ci+1 − ci), where ci is the stock price on the ith day.

2) check the evolutions of the δi, by following ∆i = δi+1 − δi.

The decision is made regarding the value of ∆. When ∆ = −2, the bot decides to buy since it indicates the end of a trough. While ∆i = 2 indicates the beginning of a dip, the bot decides to sell.

The authors explored several possibilities in the experimental study by comparing different prediction models such as single/stacked many-to-one LSTM architectures and the Encoder-Decoder model. Results demonstrate that the single/double-stacked LSTMs are the best architectures. In addition, for simplicity and speed, the prediction of multiple days together has proven to be more interesting than predicting the future day one at a time since the latter approach can only predict one day at a time. Finally, the decisions taken by the bot surpass even the most aggressive ETFs and the main investment products provided by investment firms.

We have seen in this article a new model of stock market prediction which allows benefiting from two major advantages: firstly, to predict stocks which suffers from a limited database thanks to a network trained on several other firms belonging to the same sector of activity. Second, benefit from decision support thanks to a bot that knows when to buy or sell according to daily changes in predicted closing values.

This Article is written as a research summary article by Marktechpost Staff based on the research article 'StockBot: Using LSTMs to Predict Stock Prices'. All Credit For This Research Goes To Researchers on This Project. Checkout the paper, gitlab link.Please Don't Forget To Join Our ML Subreddit

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Researchers at Stanford have developed an Artificial Intelligence (AI) model,' StockBot', which uses LSTMs to predict stock prices with gains higher than the most aggressive ETFs

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Stock market behavior forecasting is a crucial undertaking that requires careful attention since, with the right choices, a successful prediction of stock prices might result in attractive gains. Due to non-stationary, noisy, inter-dependence, and chaotic data, stock market forecasting is a significant challenge, making it difficult for investors to spend their money in a way that would result in profits.

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Electric vehicles give a lesser-known pollution headache - DW - 07/12/2023

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Electric vehicles give a lesser-known pollution headache - DW - 07/12/2023

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31 minutes ago

As EV sales have soared, the tire industry has innovated its products to fit the needs of the relatively heavy, efficient and silent cars. But there is one problem the industry is struggling to crack.

When considering the environmental impacts of cars, the focus is seldom on tires which shed small particles

As a car's only point of contact with the road, tires do more work than they get credit for. Tires need to grip the road firmly enough for cars to accelerate, turn and brake without skidding, but they also need to reduce rolling resistance enough to maintain fuel efficiency.

For tire manufacturers, creating the perfect tire - one that balances performance and durability - is an unending task. In recent years, their work has been further complicated by electric vehicles (EVs).

Due to their bigger batteries, EVs tend to be significantly heavier than their internal combustion engine counterparts. Volkswagon's e-Golf, for example, is about 400 kilograms heavier than the gas-powered Golf VII. That added weight rests on the car's tires, so EVs need tires that are sturdier.

EVs also tend to have more torque than combustion engines, which their tires need to be able to transfer to the road within seconds.

Leading tire manufacturers are working on improving tire designs, and innovating new chemical formulas to meet the needs of EVs. Some brands have introduced products specifically for use on battery-run vehicles, whereas others say they have adapted all of their tires to perform better for both EVs and combustion engine vehicles.

"We have been optimizing our product range for a long time, especially in terms of service life, rolling resistance and rolling noise - factors that are of particular benefit to electric vehicles," a spokesperson for tire maker Continental told DW in a statement.

How tires cause pollution

Considering the environmental impacts of cars, the focus tends to be on air pollution in the form of exhaust coming out of tailpipes. But tires contribute significantly.

Tires wear down over time - with every revolution, they shed tiny particles. The smallest of these pieces go into the air where they can be inhaled or drift off the road to settle on the soil nearby.

"Tire use is probably the naughtiest problem for vehicles," Nick Molden, founder and CEO of Emissions Analytics, told DW. "With a lot of other pollution, you can effectively trap it using some sort of filter or catalyst. But the tire is a fundamentally open system - you cannot encase a tire."

Emissions Analytics performs independent tests on cars including real-world tailpipe and tire emissions. It has compiled data that confirms that tire particulate pollution has significantly surpassed tailpipe emissions.

According to a report shared by Emissions Analytics, a single car sheds 4 kilograms (8.8 pounds) of tire particles weight per year, on average. Multiplied across the global fleet that equates to 6 million tons of tire particles annually.

"We measure the amount of solid material that comes out the tailpipe on the road, and we do the same measuring the mass that's shed by the tires," explained Molden. "Every year the amount coming from the tailpipe gets lower and lower, and the amount coming from tires is growing because vehicles are getting heavier."

A case study published by Emissions Analytics compared the tire emissions from a Tesla Model Y to a Kia Niro and found that Tesla's tire wear emissions were 26% greater.

Are e-cars as green as they seem?

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Environmental hazard

Tire particulate pollution has two primary negative impacts on environmental health. The particulate itself washes into waterways and has been found to be a significant source of oceanic microplastics. Also, tires contain volatile organic compounds (VOCs), which are hazardous to human health, and react in the atmosphere to create smog.

A particularly concerning chemical in tires is 6PPD, which is used to prevent the rubber from cracking or breaking down. 6PPD is also water-soluble, so it is carried off of roads by rain and washed into rivers and oceans where it has been linked to mass salmon and trout die-offs. Further studies found that 6PPD is absorbed by edible plants like lettuce and that the compound can be found in human urine.

"It's the tire industry's mission to secure a safe comfortable journey with peace of mind, and thus the addition of 6PPD to tire rubber is currently indispensable," tire maker Bridgestone told DW.

Can tires' pollution problem be solved?

Phasing out fossil fuel-powered vehicles is an urgently needed aspect of mitigating climate change. But if doing so comes with worsening tire emissions, that is also problematic.

"The obvious solution is to be driving and selling fewer cars," climate activist Tadzio Müller told DW. He added, "The shift toward electric vehicles is meant to convince us that it's going to save the planet, but of course, it cannot because the problem has always been capitalist growth."

Asked if reducing car use overall is the best solution to tires' environmental impacts, Molden told DW, "Yes, that would reduce tire emissions. But in terms of the lost economic activity, would that be worth it?"

I"It's better to create a market mechanism where it's in the interest of the tire companies to invest a lot and come up with the best formulations," Molden said, adding that currently there is a two to three times difference between certain tires in terms of VOC toxicity. Molden said generally, Europe's leading tire brands are among the best whereas cheap imports tend to be the worst.

On an individual level, avoiding rapid accelerations and immediate stops can reduce tire wear. It's also advisable to use tires to the end of their lifespan because new tires shed twice as much particulate during their first couple thousand kilometers.
Edited by: Ashutosh Pandey
Good ideas for fewer emissions

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# data[].attributes.attachment_link.title
Electric vehicles give a lesser-known pollution headache - DW - 07/12/2023

# data[].attributes.attachment_link.description
As EV sales have soared, the tire industry has innovated its products to fit the needs of the relatively heavy, efficient and silent cars. But there is one problem the industry is struggling to crack.