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Renewable energy deal aims to take Google’s UK operations to 90% carbon-free by 2025

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Google UK has claimed it will operate at, or near, 90% carbon-free energy (CFE) on an hourly basis in 2025, from a projected 67%, thanks to a new offshore wind energy deal with ENGIE – 100 MW of carbon-free energy from Moray West offshore wind farm in Scotland. 

This milestone brings Google closer to its overarching goal of operating on carbon-free energy by 2030 across its UK offices and cloud regions. By achieving this goal, every Gmail sent, every Search query asked and every YouTube video watched, will be powered by cloud regions that run on carbon-free energy every hour of every day.

This Power Purchase Agreement (PPA) will establish the UK as one of our cleanest cloud regions not just in Europe, but in the world. This is particularly timely given the rapid expansion of computing as more British businesses look to digitally transform their operations with Google Cloud.

Google’s PPA with ENGIE will make the UK one of six Google Cloud regions – including Finland, Iowa, Montreal, Spain and Toronto – operating at or near 90% CFE, on an hourly basis, when the PPA goes live in 2025.

Carbon-free energy deals like this one are critical to the growth of the UK’s renewable energy sector: the investments add more renewable energy to the grid by funding the construction of new sites. These deals increase the country’s renewable energy sources, support long-term energy security goals and help to guarantee financing for much of the upfront cost. Deals such as these support Prime Minister Rishi Sunak’s ‘pledge to make the UK a clean energy superpower’ and deliver on Scotland’s aim to be an important location for offshore wind innovation, research and development. 

Matt Brittin, president of Google EMEA, said: “People across the UK and Europe are increasingly worried about climate change and energy security. We share that concern and believe technology is an important part of the solution – both by reducing our own emissions, and by helping others to reduce theirs.

This new investment in UK renewable energy brings us one step closer to reaching our goal of operating entirely on carbon-free energy by 2030, and means that, in the UK, we’ll be running on at or near 90% carbon-free energy in 2025.”

Paulo ALMIRANTE, ENGIE senior EVP in charge of Renewables, Energy Management and Nuclear Activities, said: “We are very pleased to support Google in its progress towards its carbon-free target. As a major player in PPAs, ENGIE designs innovative green corporate PPAs for off-takers from offshore wind, thanks to the strong development of Ocean Winds, as we do for our onshore renewable projects. This contract demonstrates the strength of the Group’s renewable portfolio, with 37 GW of renewable assets, a major advantage for our customers in their decarbonization strategy.”

This milestone is the latest step in Google’s goal to fully decarbonise by 2030. The company’s climate action strategy focuses on three key areas: firstly, making Google’s operations more sustainable including through innovation in energy sourcing technology, such as Google Cloud and ENGIE’s artificial intelligence project that optimises wind energy use. Secondly, supporting other organisations and cities as they decarbonise – for example Google recently partnered with C40 Cities and London Mayor Sadiq Khan to launch a first-of-its-kind 24/7 Carbon-Free Energy for Cities pilot program. 

And thirdly, empowering consumers to make more sustainable choices. We have launched fuel-efficient routing, giving people the option to choose routes optimised for lower fuel consumption. Earlier this month, Google also launched an energy information feature on Search across Europe, to help people quickly find authoritative information on saving energy and local support.

This PPA is the latest in a number of long-term investments that Google has made in the UK. In January this year, we announced the purchase of our office in Central Saint Giles, and unveiled plans to expand our presence in the UK. In July, we marked the topping out of our new site in King’s Cross.

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Why Decision Intelligence Is Important

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Why Decision Intelligence Is Important

Traditional decision-making techniques will lose effectiveness as firms become more complicated.

Tech leaders must use decision intelligence models to enable exact and contextualized decisions.

Because it is difficult to identify potential disconnections associated with behavioral models in a commercial environment, many current decision-making models need to be more logical. With the aid of machine learning and AI algorithms, decision intelligence may help organizations make better decisions. Decision intelligence is the use of automation and machine learning to support human judgments in order to make business judgments that are more accurate and swift.

To do this, decision intelligence gives anyone the tools to ask and respond to ‘what, why, and how’ kinds of questions of unaggregated data, significantly decreasing the time and effort required to develop strategic, operational decisions. Employing decision intelligence promotes automation without undervaluing the importance of human judgment, expertise and intuition. However, businesses must persistently work to increase their daily operations’ productivity and eradicate bias in their decision-making processes. By utilizing data analytics, artificial intelligence and machine learning for precise decision-making, decision intelligence can assist businesses in accomplishing more with less.

Why Decision Intelligence Is Important

Decision intelligence becomes extremely important as it enables businesses to make accurate decisions. Below are a few pointers of decision intelligence that make it a critical decision-making tool for businesses to succeed.

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To Rethink

Today’s work is frequently dull and unfulfilling, negatively affecting productivity and happiness. Due to the time saved by decision intelligence, businesses will eventually need to stop defining people by their employment and think about decreasing dull work. This will enable them to produce more purposeful, original and creative work.

To Redefine

Decision intelligence is changing the way we learn and think. Analyzing data, gaining insights, creating forecasts, and assisting in decision-making are all aspects of thinking and learning skills. By combining and unlocking the distribution of distinctive information, data intelligence frameworks help to redefine and increase the breadth of end-to-end business workflows.

To Reinvent

Data intelligence will be highly crucial and beneficial for businesses to strengthen their competitive edge, develop detailed customer segmentation, anticipate market needs, and develop customer-centric strategies. Thereby, it can assist businesses in reinventing new customer acquisition business models.

To Repurpose

A data-driven environment with a technology base that enables organizations to synthesize information, learn from it, and apply insights at scale can be built by firms through leveraging decision intelligence. This will accelerate decision-making, increase agility and resilience and develop a business culture that supports organizational-driven initiatives with a purpose.

 

Decision intelligence does not eliminate human judgment from the decision-making process. Instead, it is vital to equip humans with AI and a more comprehensive, approachable perspective of all the data pertaining to their businesses so that they can make effective business decisions. It allows businesses to process and forecast data in order to make more educated decisions at every level of the organization. Additionally, it assists organizations in gaining better visibility into their operations and producing game-changing business results. Furthermore, with tons of data insights to consider in the decision-making process in this digital era, the next stage of digital transformation will involve having assistance in making wise decisions. Decision intelligence will enable businesses to deliver predictive outcomes as data and insights become more crucial.

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NLP & Computer Vision in Cybersecurity

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NLP & Computer Vision in Cybersecurity

Natural language processing (NLP) and computer vision are two branches of artificial intelligence (AI) that are disrupting cybersecurity.

NLP is the ability of computers to understand and process human language, including speech and text. In cybersecurity, NLP can be used for fraud detection by analyzing large amounts of text data, such as emails and chat logs, to identify patterns of malicious activity. NLP can also be used for threat intelligence by analyzing data from various sources, such as news articles and social media, to identify potential security threats.

Computer vision, on the other hand, refers to the ability of computers to interpret and understand images and videos. In cybersecurity, computer vision can be used for password cracking by analyzing images and videos that contain passwords or other sensitive information. It can also be used for facial recognition, which verifies the identity of individuals who access sensitive information or systems.

Cybersecurity is a critical issue in our increasingly connected world, and artificial intelligence (AI) is playing an increasingly important role in helping to keep sensitive information and systems secure. In particular, natural language processing (NLP) and computer vision are two areas of AI that are having a major impact on cybersecurity.

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Source: Masernet

NLP and computer vision have the potential to revolutionize the way organizations approach cybersecurity by allowing them to analyze large amounts of data, identify patterns of malicious activity, and respond to security threats more quickly and effectively. However, it’s important to be aware that AI itself presents new security risks, such as the potential for AI systems to be hacked or misused. As a result, organizations must adopt a comprehensive and well-informed approach to cybersecurity that takes into account the full range of risks and benefits associated with AI technologies. Here are 4 ways NLP & computer vision are useful in cybersecurity.

1. Detecting Fraud

NLP can be used to analyze large amounts of text data, such as emails and chat logs, to identify patterns of fraud and other types of malicious activity. This can help organizations to detect and prevent fraud before it causes significant harm.

2. Analyzing Threats

NLP can also be used to analyze large amounts of text data from a variety of sources, such as news articles and social media, to identify potential security threats. This type of “big data” analysis can help organizations to respond to security threats more quickly and effectively.

3. Preventing Password Cracking

Computer vision can be used to crack passwords by analyzing images and videos that contain passwords or other sensitive information. This type of technology can help organizations to better protect their sensitive information by making it more difficult for attackers to obtain passwords through visual means.

4. Improving Facial Recognition

Computer vision can also be used for facial recognition, which can help organizations to improve their security by verifying the identity of individuals who access sensitive information or systems.

Conclusion

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Source: Visua

AI technologies like NLP and computer vision are playing an increasingly important role in helping to keep sensitive information and systems secure. These technologies have the potential to revolutionize the way that organizations approach cybersecurity by allowing them to analyze large amounts of data, identify patterns of malicious activity, and respond to security threats more quickly and effectively. However, it’s also important to recognize that AI itself presents new security risks, such as the potential for AI systems to be hacked or misused. As a result, organizations must take a holistic and well-informed approach to cybersecurity that takes into account the full range of risks and benefits associated with these powerful new technologies.

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What’s Wrong with the Algorithms?

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What's Wrong with the Algorithms?

Social media algorithms have become a source of concern due to the spread of misinformation, echo chambers, and political polarization.

The main purpose of social media algorithms is to personalize and optimize user experience on platforms such as Facebook, Twitter, and YouTube.

Most social media algorithms sort, filter, and prioritize content based on a user’s individual preferences and behaviors. Social media algorithms have come under scrutiny in recent years for contributing to the spread of misinformation, echo chambers, and political polarization.

Facebook’s news feed algorithm has been criticized for spreading misinformation, creating echo chambers, and reinforcing political polarization. In 2016, the algorithm was found to have played a role in the spread of false information related to the U.S. Presidential election, including the promotion of fake news stories and propaganda. Facebook has since made changes to its algorithm to reduce the spread of misinformation, but concerns about bias and polarization persist.

Twitter’s trending topics algorithm has also been criticized for perpetuating bias and spreading misinformation. In 2016, it was revealed that the algorithm was prioritizing trending topics based on popularity, rather than accuracy or relevance. This led to the promotion of false and misleading information, including conspiracy theories and propaganda. Twitter has since made changes to its algorithm to reduce the spread of misinformation and improve the quality of public discourse.

YouTube’s recommendation algorithm has been criticized for spreading conspiracy theories and promoting extremist content. In 2019, it was revealed that the algorithm was recommending conspiracy theory videos related to the moon landing, 9/11, and other historical events. Additionally, the algorithm was found to be promoting extremist content, including white nationalist propaganda and hate speech. YouTube has since made changes to its algorithm to reduce the spread of misinformation and extremist content, but concerns about bias and polarization persist.

In this article, we’ll examine the problem with social media algorithms including the impact they’re having on society as well as some possible solutions.

1. Spread of Misinformation

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Source: Scientific American

One of the biggest problems with social media algorithms is their tendency to spread misinformation. This can occur when algorithms prioritize sensational or controversial content, regardless of its accuracy, in order to keep users engaged and on the platform longer. This can lead to the spread of false or misleading information, which can have serious consequences for public health, national security, and democracy.

2. Echo Chambers and Political Polarization

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Source: PEW Research Center

Another issue with social media algorithms is that they can create echo chambers and reinforce political polarization. This happens when algorithms only show users content that aligns with their existing beliefs and values, and filter out information that challenges those beliefs. As a result, users can become trapped in a self-reinforcing bubble of misinformation and propaganda, leading to a further division of society and a decline in the quality of public discourse.

3. Bias in Algorithm Design and Data Collection

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Source: Springer Link

There are also concerns about bias in the design and implementation of social media algorithms. The data used to train these algorithms is often collected from users in a biased manner, which can perpetuate existing inequalities and reinforce existing power structures. Additionally, the designers and developers of these algorithms may hold their own biases, which can be reflected in the algorithms they create. This can result in discriminatory outcomes and perpetuate social injustices.

4. Democracy in Retreat

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Source: Freedom House

Social media algorithms are vulnerable to manipulation and can spread false or misleading information, which can be used to manipulate public opinion and undermine democratic institutions. The dominance of a few large social media companies has led to a concentration of power in the hands of a small number of organizations, which can undermine the diversity and competitiveness of the marketplace of ideas, a key principle of democratic societies.

How to Improve Social Media Algorithms?

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Source: Tech Xplore

Governments and regulatory bodies have a role to play in holding technology companies accountable for the algorithms they create and their impact on society. This could involve enforcing laws and regulations to prevent the spread of misinformation and extremist content, and holding companies responsible for their algorithms’ biases.

There are several possible solutions that can be implemented to improve social media algorithms and reduce their impact on democracy. Some of these solutions include:

  • Increased transparency and accountability: Social media companies should be more transparent about their algorithms and data practices, and they should be held accountable for the impact of their algorithms on society. This can include regular audits and public reporting on algorithmic biases and their impact on society.

  • Regulation and standards: Governments can play a role in ensuring that social media algorithms are designed and operated in a way that is consistent with democratic values and principles. This can include setting standards for algorithmic transparency, accountability, and fairness, and enforcing penalties for violations of these standards.

  • Diversification of ownership: Encouraging a more diverse and competitive landscape of social media companies can reduce the concentration of power in the hands of a few dominant players and promote innovation and diversity in the marketplace of ideas.

  • User education and awareness: Social media users can be educated and empowered to make informed decisions about their usage of social media, including recognizing and avoiding disinformation and biased content.

  • Encouragement of responsible content creation: Social media companies can work to encourage the creation of high-quality and responsible content by prioritizing accurate information and rewarding creators who produce this content.

  • Collaboration between industry, government, and civil society: Addressing the challenges posed by social media algorithms will require collaboration between social media companies, governments, and civil society organizations. This collaboration can involve the sharing of data and best practices, the development of common standards and regulations, and the implementation of public education and awareness programs.

Conclusion

Social media companies have the power to censor and suppress speech, which can undermine the right to free expression and the democratic principle of an open and inclusive public discourse. It is crucial for technology companies and policymakers to address these issues and work to reduce the potential for harm from these algorithms. Social media platforms need to actively encourage and facilitate community participation in the development and improvement of their algorithms. This would involve setting up forums for discussion and collaboration, providing documentation and support for developers, and engaging with the community to address their concerns and ideas. In order to ensure that the algorithms are fair and unbiased, tech companies need to be transparent about the data they collect and use to train their algorithms. This would involve releasing the data sets used to train the algorithms, along with information about how the data was collected, what it represents, and any limitations or biases it may contain.

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