Robots in a news room
  1. OpenAI Breach: OpenAI’s systems were recently breached. However, the breach was superficial and did not compromise any secret ChatGPT conversations [1]. 
  2. Quantum Rise Funding: Quantum Rise, a Chicago-based startup that provides AI-driven automation for companies, raised a $15 million seed round [1]. 
  3. AI Regulation: The U.S. Supreme Court struck down “Chevron deference,” a 40-year-old ruling on federal agencies’ power [1]. 
  4. AI Deepfakes: YouTube has made changes to make it easier to report and take down AI deepfakes [1]. 
  5. Cloudflare’s AI Bot Tool: Cloudflare launched a tool to combat AI bots [1]. 
  6. Altrove’s New Materials: Altrove, a French startup, is using AI models and lab automation to create new materials [1]. 
  7. AI’s Energy Cost: Google’s environmental report avoided addressing the actual energy cost of AI [1]. 
  8. Generative AI in Metaverse Games: Meta plans to bring more generative AI tech into games, specifically VR, AR (Augmented Reality), and mixed reality games [1]. 
  9. Plagiarism Accusations: News outlets are accusing Perplexity of plagiarism and unethical web scraping [1]. 
  10. Hebbia’s Funding: AI startup Hebbia raised $130 million in funding. The company aims to help firms efficiently parse and interpret complex data [5]. 

                    For more details, refer to the source below.

                    Sources:  

                    1. AI News & Artificial Intelligence | TechCrunch 
                    2. AI News Today – July 9th, 2024 – The Dales Report 
                    3. The most important AI trends in 2024 – IBM Blog 
                    4. AI News June 2024: In-Depth and Concise 
                    5. June 2024: Top five AI stories of the month – FinTech Futures: Fintech news 
                            1. Data Analysis and Predictions: AI’s ability to analyze vast amounts of data, identify patterns, and make predictions has revolutionized change management [2]. By harnessing AI-powered tools and technologies, organizations can navigate transitions more efficiently, mitigate risks, and maximize the chances of successful outcomes [2]. 
                            1. Understanding the Current State and Anticipating Future Challenges: AI provides analytics and insights derived from data gathered from various sources, including internal systems, customer interactions, market trends, and even social media platforms [2]. Through machine learning, natural language processing, and predictive modeling, AI can uncover hidden patterns, identify potential bottlenecks, and forecast the impact of proposed changes accurately and quickly [2]. 
                            1. Stakeholder Communication and Collaboration: AI-powered tools and platforms facilitate stakeholder communication and collaboration throughout the change process [2]. Virtual assistants, chatbots, and collaboration platforms equipped with AI capabilities can provide personalized support, answer queries, and disseminate relevant information in real-time, fostering transparency and engagement among employees at all levels [2]. 
                            1. Sentiment Analysis: AI-driven sentiment analysis can gauge the emotional pulse of the workforce, enabling leaders to address concerns proactively and tailor their communication strategies to alleviate resistance and build trust [2]. 
                            1. Change Management Expertise: Demographic patterns in the data show that expert change management professionals with more than five years of experience use AI in their practice more than novices [1]. Professionals with split responsibilities—as strategy consultants, business leaders, project managers or executive sponsors—also report higher AI usage in their work [1]. 

                            It is important to note that there are challenges to AI adoption in change management. These include a lack of understanding about how to use AI effectively, inadequate experience with AI, fear of unidentified risks, limited access to tools and resources for applying AI in change management, and concerns about data privacy and security [1]. 

                            AI is not only often the catalyst behind the need to change, but it is also shifting the way that organizations manage change [4]. With the right communication and integration plan, AI can be used to enhance productivity, performance, and agility at both the organizational and individual levels [4]. 

                            Sources:  

                            1. AI in Change Management: Use Cases, Applications, Implementation and … 
                            1. AI in Change Management: Early Findings, Challenges and Opportunities 
                            1. 5 ways to think about AI’s role in change management | HR Dive 
                            1. AI in Change Management: Use Cases, Applications, Implementation and … 
                            1. AI and Change Management | SpringerLink 

                            According to the U.S. Bureau of Labor Statistics, as of 2021 there are five generations of workers: Traditionalists, also known as The Silent Generation (born before 1946), Baby Boomers (born between 1946 and 1964), Generation X (born between 1965 and 1980), Millennials (born between 1981 and 1996), and Generation Z (born after 1996). 

                            This generational diversity can bring benefits to organizations, such as increased creativity, innovation, and productivity. However, it can also pose some unique challenges for employers and managers who need to manage and motivate a multigenerational workforce and it could take some time to overcome them. 

                            Communication Styles 

                            One of the most obvious challenges of having a multigenerational workforce is the difference in communication styles. Each generation has its own preferences and habits for communicating, both verbally and non-verbally. For example, Traditionalists and Baby Boomers tend to favor formal and face-to-face communication, while Generation X and Millennials prefer informal and digital communication, such as email, text, or social media. Generation Z, the newest generation in the workforce, is even more tech-savvy and accustomed to using multiple platforms and devices for communication. 

                            Managers need to be aware of the communication preferences of each generation and adapt accordingly. They also need to foster a culture of respect and openness, where employees can express their opinions and feedback without fear of judgment or ridicule. Moreover, managers need to encourage cross-generational communication and collaboration, by creating opportunities for employees to interact and learn from each other, such as mentoring programs, team projects, or social events. 

                            Technological Adaptation 

                            Another challenge that stems from having a multigenerational workforce is the difference in technological adaptation. Technology plays a vital role in the modern workplace, as it enables faster, easier, and more efficient processes and outcomes. However, not all generations are equally comfortable with using and learning technology. Younger generations are more adept at embracing and adopting modern technologies, while older generations may struggle or resist them. 

                            To overcome this challenge, managers need to provide adequate training and support for employees who need to learn new technologies. They also need to explain the benefits and rationale behind the introduction of new technologies, and how they can enhance the work experience and performance of employees. Furthermore, managers need to leverage the strengths and skills of each generation, by assigning tasks and roles that match their technological capabilities and preferences. 

                            Workplace Expectations 

                            A third challenge that arises from having a multigenerational workforce is the difference in workplace expectations. Each generation has its own expectations and values regarding work-life balance, job stability, career progression, and organizational loyalty.  

                            Traditionalists and Baby Boomers tend to value job security, long-term commitment, and hierarchical structures, while Generation X and Millennials tend to value flexibility, autonomy, and horizontal structures. Generation Z, the youngest generation in the workforce, tends to value purpose-driven work, diversity, and social responsibility. 

                            To overcome this challenge, managers need to understand the expectations and values of each generation and align them with the organizational goals and vision. They also need to provide a variety of rewards and incentives that appeal to different generations, such as financial compensation, recognition, feedback, development opportunities, or work-life balance options. Additionally, managers need to create a culture of trust and transparency, where employees can voice their concerns and expectations and feel valued and respected. 

                            Motivational Factors 

                            A fourth challenge that emerges from having a multigenerational workforce is the difference in motivational factors. Each generation has its own sources of motivation and engagement at work, which can influence their performance and satisfaction.  

                            Traditionalists and Baby Boomers tend to be motivated by duty, respect, and achievement, while Generation X and Millennials tend to be motivated by challenge, feedback, and growth. Generation Z, the most recent generation in the workforce, tends to be motivated by impact, meaning, and social good. 

                            To overcome this challenge, managers need to identify the motivational factors of each generation and tailor their leadership style accordingly. They also need to provide a clear and compelling vision and mission for the organization and show how each employee contributes to it. Moreover, managers need to empower and involve employees in decision-making and problem-solving, and 

                            Having five generations in the workforce presents unique challenges for employers and managers. Here are some of the key challenges: 

                            • Communication Styles: Each generation has its preferred methods of communication, from traditional face-to-face interactions to digital messaging. Balancing these preferences can be tricky. 
                            • Technological Adaptation: Younger generations are typically more comfortable with rapidly changing technology, while older generations may prefer traditional methods. Integrating new technologies in a way that works for everyone requires careful planning. 
                            • Workplace Expectations: Different generations have varying expectations regarding work-life balance, job stability, and career progression. Aligning these expectations with organizational goals is a complex task. 
                            • Motivational Factors: What motivates employees can differ significantly across generations. For example, some may value job security, while others prioritize flexibility or purpose-driven work. 
                            • Resistance to Change: Change can be met with resistance, especially if it affects personal lives. Understanding and managing this resistance is crucial for successful organizational change. 
                            • Diversity and Inclusion: Embracing generational diversity and creating an inclusive environment is essential. It involves recognizing and valuing the unique contributions of each age group. 
                            • Succession Planning: With a wide age range, succession planning becomes more critical. Organizations must consider all age groups to ensure a smooth transition and continuity. 
                            • Learning and Mentoring: There are opportunities for cross-generational learning and mentoring, but facilitating these relationships effectively can be challenging. 
                            • Managing Conflict: Different life experiences influence how individuals handle conflict. A multigenerational workforce may require varied approaches to conflict resolution. 
                            • Employers who successfully navigate these challenges can harness the strengths of a diverse workforce, leading to increased innovation, productivity, and employee satisfaction [1] [2]. 

                            Sources: 

                            1. Generational Differences in the Workplace [Infographic]

                            2. Multigenerational Workforce: Benefits, Challenges, and 9 Best … – AIHR 

                            3. Bridging Generational Divides in Your Workplace – Harvard Business Review 

                            AI generated image of a robot in a laptop

                            The workforce of today is more diverse than ever before. It consists of people from diverse backgrounds, cultures, and genders, and from different generations. According to the U.S. Bureau of Labor Statistics, as of 2021 there are five generations of workers and this can bring many benefits to organizations, such as increased creativity, innovation, and productivity. However, it can also pose some unique challenges for employers and managers who need to manage and motivate a multigenerational workforce especially with their acceptance of technology and AI (Artificial Intelligence) is no different. 

                            1. Silent Generation (Born 1928-1945): Members of the Silent Generation tend to report being significantly less knowledgeable about AI [14]. They are slower to adapt to major technological changes [15]. 
                            1. Baby Boomers (Born 1946-1964): Boomers are more skeptical about AI. Only 38% of Boomers believe AI will have a positive impact on their line of work [1]. They are selective in the use of new and emerging technologies [4] and are less enthusiastic about AI [3]. 
                            1. Generation X (Born 1965-1980): Gen X is mixed in their acceptance of AI. 45% of Gen X members believe AI will have a positive impact on their line of work [1]. However, they are also less enthusiastic about AI compared to younger generations [10]. 
                            1. Millennials (Born 1981-1996): Millennials are more optimistic about AI. 62% of Millennials believe AI will have a positive impact on their line of work [1] [13]. They are already using AI tools at work in a variety of use cases [1]. 
                            1. Generation Z (Born 1997-2012): Gen Z is expected to be the most exposed to AI and is likely to actively utilize AI in their work [10]. They are also concerned about the ethical and privacy issues related to AI [11]. 

                            Please note that these are general trends and individual attitudes towards AI can vary. Also, AI acceptance can and will change over time as technology evolves. 

                            Sources:  

                            1. AI and longevity – Massachusetts Institute of Technology 
                            2. Trust in Artificial Intelligence: Global Insights 2023 – KPMG 
                            3. The AI Generation Gap: Millennials Embrace AI, Boomers Are … – PCMag 
                            4. From Boomers To Gen Z: How Different Generations Adapt And … – Epsilon 
                            5. Who’s Really on Board with AI: Youngsters or Boomers?” 
                            6. Gen Z Will Shape The Age Of AI – Forbes 
                            7. The AI Generation Gap: Millennials Embrace AI, Boomers Are Skeptical 
                            8. Emotional AI and gen Z: The attitude towards new technology and its … 
                            9. Why Gen X and boomers stand to benefit from the use of AI in the … – MSN 
                            10. The AI Generation Gap: Millennials Embrace AI, Boomers Are … – PCMag 
                            11. The AI Generation Gap: Millennials Embrace AI, Boomers Are Skeptical 
                            12. Gen Z students worry about AI, student debt, and careers 
                            13. GenZ embraces ‘human machine symbiosis’ as 72% believe AI understands them better than anyone: Cheil report 
                            14. AI skills can help you land a job or promotion faster—especially for Gen Z, says new research 
                            15. Gen Z AI: The Rising Generation’s Connection with Artificial … 
                            16. Acceptance of Generative AI in the Creative Industry: Examining the … 
                            17. Trust in AI tools like ChatGPT is high among Gen Z — but Gen X and … 
                            AI Generated image of US Capitol

                            What is the APRA? 

                            The American Privacy Rights Act (APRA) is a proposed federal legislation that aims to regulate the collection, use, and sharing of personal data by online platforms and service providers. The bipartisan bill was introduced in April 2024 by Representative Cathy McMorris Rodgers (R-WA) and Senator Maria Cantwell (D-WA) and is currently under review by the Senate Commerce Committee.  

                            Under the proposed American Privacy Rights Act (APRA), there are several ways you can opt out of data sales: 

                            1. Opting out of data transfer and targeted advertising: For most covered data, covered entities would need to give individuals an opportunity to opt out of the transfer of their covered data or the use of their data for targeted advertising [5]. 
                            2. Express consent for sensitive data: For sensitive covered data, covered entities would be required to obtain an individual’s affirmative, express consent before transferring that data [5]. 
                            3. Data brokers: Data brokers would be required to register with the FTC, which would establish a central data broker registry with a “Do Not Collect” mechanism allowing individuals to opt out of data brokers’ collection of their covered data [6]. 
                            4. Website for opt-out requests: Under the APRA, data brokers will need to maintain a website that identifies themselves as data brokers, provides a tool for subject rights and opt-out requests, and links to the FTC’s data broker registry [4]. 

                              Opposition 

                              The Electronic Frontier Foundation (EFF) has expressed opposition to the American Privacy Rights Act (APRA) for several reasons [8] [9]: 

                              • Rolling back state privacy protections: The EFF believes that federal privacy laws should not roll back state privacy protections [8] [9]. They argue that there is no reason to trade strong state laws for weaker national privacy protection [9]. 
                              • Overriding stronger state laws: The EFF opposes APRA because it overrides stronger state laws and prevents states from passing stronger laws, which they believe hurts everyone [8]. 
                              • Concerns about the latest draft: The EFF, along with other advocacy groups, have raised concerns about the latest draft of the APRA. They claim that the latest revision has diluted the privacy rules [7]. For example, the new draft allegedly strips out anti-discrimination protections, AI impact assessment requirements, and the ability to opt-out of AI decision-making for major economic opportunities like housing and credit [7]. 
                              • Loopholes in personal data collection: The EFF is concerned that the latest APRA revision fails to cover personal data collected and used on-device [7]. They argue that tech companies would be able to do almost anything they want with data that stays on a personal device – no data minimization rules, no protections for kids, no advertising limits, no transparency requirements, no civil rights safeguards, and no right to sue for injured consumers [7]. 

                              Please note that the APRA is still a proposed bill and has not yet become law. The final Act, if approved, may have different provisions [1] [2]. It’s always a good idea to consult with a legal professional for advice tailored to your specific situation. 

                              Microsoft Copilot

                              About Microsoft Copilot


                              Microsoft Copilot is a generative artificial intelligence chatbot developed by Microsoft. Launched in February 2023 as Microsoft’s primary replacement for the discontinued Cortana. The service was initially called Bing Chat and was featured as a built-in feature for Microsoft’s search engine Bing and Microsoft’s web browser Edge.

                              Microsoft Copilot sells the power of its AI to boost “productivity, unlock creativity, and help you understand information better with a simple chat experience.” It coordinates large language models (LLMs), content in Microsoft Graph, and Microsoft 365 productivity apps, such as Word, Excel, PowerPoint, Outlook, Teams, and others.

                              There are different versions of Copilot:

                              • Copilot Free: The basic version that allows you to create original content and answer questions.
                              • Copilot Pro: A more robust version for your creativity and productivity that costs $20/month.
                              • Copilot for Microsoft 365: This version is optimized for your organization’s Microsoft 365 Business Standard or Business Premium subscription.

                              Using Microsoft Copilot to generate an image.

                              Click on Copilot and you will see a place for a prompt. You can experiment with the creative levels.

                              Enter your prompt, to generate an image

                              Your image will render

                              Up to four images will render

                              Copilot will make some suggestions to change your image

                              The final output



                              Image of a record player with records

                              Introduction 

                              Artificial intelligence (AI) has been used to create background music, enhance existing songs, or compose original melodies. However, some AI music platforms have been accused of violating the copyrights of major record labels, who claim that the AI-generated music infringes on their original works. 

                              The plaintiffs, Universal Music Group, Sony Music Entertainment, and Warner Music Group have countered that the AI systems used by the defendants are not capable of generating truly original and creative music and that they rely on the musical data and inputs provided by the plaintiffs and other sources. They have also asserted that their songs have distinctive and recognizable features that are copied or reproduced by the AI systems without authorization. 

                              The lawsuits have raised complex and novel legal issues regarding the nature and scope of copyright protection for AI-generated music, and the criteria and standards for determining the originality, creativity, and ownership of AI-generated music. The outcomes of the lawsuits could have significant implications for the future of the AI music industry and the rights and interests of the musicians, composers, producers, and consumers involved. 

                              Examples of AI Music Platforms Sued by Major Record Labels 

                              • Amper Music: Amper Music is an AI music platform that allows users to create custom music for their videos, podcasts, games, or other projects. In 2020, Amper Music was sued by Universal Music Group, Sony Music Entertainment, and Warner Music Group, who alleged that Amper Music’s AI system copied the melodies, rhythms, harmonies, and lyrics of their songs without authorization. 
                              • Mubert: Mubert is an AI music platform that generates adaptive music streams for various scenarios, such as meditation, fitness, gaming, or studying. In 2021, Mubert was sued by Sony Music Entertainment, who claimed that Mubert’s AI system used the samples, loops, and stems of their songs without permission. 
                              • Boomy: Boomy is an AI music platform that enables users to create and sell their own songs, which are generated by an AI system based on the user’s preferences and inputs. In 2021, Boomy was sued by Warner Music Group, who alleged that Boomy’s AI system reproduced the melodies, structures, and styles of their songs without consent. 

                              Conclusion 

                              AI music platforms have been facing legal challenges from major record labels, who argue that the AI-generated music infringes on their copyrights. The lawsuits raise questions about the originality, creativity, and ownership of AI-generated music, and how the existing laws and regulations can address these issues. 

                              An AI generated image of Target

                              Introduction 

                              Artificial intelligence (AI) is transforming the way businesses operate, from enhancing customer experience to optimizing supply chain management. But AI is not only used for external purposes, but it is also applied internally to improve the productivity, performance, and well-being of employees. In this document, we will explore how companies are using AI with their employees, and examine the case of Target, a leading retailer that has implemented various AI initiatives to empower its workforce. 

                              How Companies Are Using AI with Their Employees 

                              According to a report by IBM, 74% of global CEOs say that AI will play a key role in their ability to provide a better work environment for their employees in the next two to three years. Some of the ways that companies are using AI with their employees are: 

                              • AI for recruitment and hiring: AI can help companies streamline the hiring process, by automating tasks such as screening resumes, scheduling interviews, and assessing candidates. AI can also help reduce bias and increase diversity in hiring, by using data-driven algorithms and natural language processing to evaluate candidates based on their skills and potential, rather than their demographics or background. 
                              • AI for learning and development: AI can help companies provide personalized and adaptive learning experiences for their employees, by analyzing their learning preferences, goals, and progress, and recommending relevant content, courses, and mentors. AI can also help create interactive and engaging learning environments, by using gamification, simulations, and virtual reality to enhance the learning outcomes. 
                              • AI for performance and feedback: AI can help companies measure and improve the performance and feedback of their employees, by using data analytics, sentiment analysis, and natural language generation to provide real-time and actionable insights. AI can also help create a culture of continuous feedback and recognition, by using chatbots, voice assistants, and social media platforms to facilitate communication and collaboration among employees and managers. 
                              • AI for well-being and engagement: AI can help companies support the well-being and engagement of their employees, by using sensors, wearables, and biometrics to monitor their physical and mental health, and provide personalized interventions and recommendations. AI can also help create a positive and inclusive work environment, by using emotion recognition, natural language understanding, and personality profiling to understand the emotions, needs, and values of employees, and provide them with tailored support and guidance. 

                              A Case Study of Target 

                              Target is one of the largest retailers in the United States, with more than 1,900 stores and 350,000 employees (about half the population of Vermont). Target has been investing in AI to enhance its customer experience, such as using computer vision to create smart shelves and using natural language processing to create voice-activated shopping lists. But Target has also been using AI to improve its employee experience, such as using machine learning to create dynamic schedules and using natural language generation to create personalized career paths. Some of the AI initiatives that Target has implemented to empower its employees are: 

                              • AI for recruitment and hiring: Target has partnered with HireVue, an AI-powered video interviewing platform, to streamline its hiring process, especially for seasonal workers. Target uses HireVue to screen candidates based on their video responses and rank them based on their fit for the role and the company culture. Target has also partnered with Eightfold, an AI-powered talent intelligence platform, to reduce bias and increase diversity in hiring, by using data-driven algorithms and natural language processing to match candidates with the best opportunities and provide them with feedback and guidance. 
                              • AI for learning and development: Target has partnered with Axonify, an AI-powered microlearning platform, to provide personalized and adaptive learning experiences for its employees, especially for frontline workers. Target uses Axonify to deliver bite-sized and gamified learning content, based on the employees’ roles, goals, and knowledge gaps. Target has also partnered with Degreed, an AI-powered learning experience platform, to create interactive and engaging learning environments, by using simulations, virtual reality, and augmented reality to enhance the learning outcomes. 
                              • AI for performance and feedback: Target has partnered with Perceptyx, an AI-powered employee survey platform, to measure and improve the performance and feedback of its employees, especially for remote workers. Target uses Perceptyx to collect and analyze employee feedback, using data analytics, sentiment analysis, and natural language generation to provide real-time and actionable insights. Target has also partnered with Workhuman, an AI-powered social recognition platform, to create a culture of continuous feedback and recognition, by using chatbots, voice assistants, and social media platforms to facilitate communication and collaboration among employees and managers. 
                              • AI for well-being and engagement: Target has partnered with Thrive Global, an AI-powered well-being platform, to support the well-being and engagement of its employees, especially during the COVID-19 pandemic. Target uses Thrive Global to monitor and improve the physical and mental health of its employees, by using sensors, wearables, and biometrics to provide personalized interventions and recommendations. Target has also partnered with Glint, an AI-powered employee engagement platform, to create a positive and inclusive work environment, by using emotion recognition, natural language understanding, and personality profiling to understand the emotions, needs, and values of employees, and provide them with tailored support and guidance. 

                              Conclusion 

                              AI is a tool for enhancing customer experience and a catalyst for improving employee experience. By using AI with their employees, companies can not only increase their efficiency and effectiveness but also their creativity and innovation. Target is a prime example of a company that has leveraged AI to empower its workforce and create a competitive advantage in the retail industry. 

                              For more information

                              AI Generated image of multiple colors with different colored blocks

                              Organizations can measure and track bias in their AI systems by implementing a combination of strategies: 

                              • AI Governance: Establishing AI governance frameworks to guide the responsible development and use of AI technologies, including policies and practices to identify and address bias [1] [2]. 
                              • Bias Detection Tools: Utilizing tools like IBM’s AI Fairness 360 toolkit, which provides a library of algorithms to detect and mitigate bias in machine learning models [1]. 
                              • Fairness Metrics: Applying fairness metrics that measure disparities in model performance across different groups to uncover hidden biases [3]. 
                              • Exploratory Data Analysis: Conducting exploratory data analysis to reveal any underlying biases in the training data used for AI models [3]. 
                              • Interdisciplinary Collaboration: Promoting collaborations between AI researchers and domain experts to gain insights into potential biases and their implications in specific fields [4]. 
                              • Diverse Teams: Involving diverse teams in the development process to bring a variety of perspectives and reduce the risk of biased outcomes [5]. 

                              These measures help organizations to actively monitor and mitigate bias, ensuring their AI systems are fair and equitable. 

                              Sources: 

                              1. IBM Policy Lab: Mitigating Bias in Artificial Intelligence 

                              2. What Is AI Bias? | IBM 

                              3. Testing AI Models — Part 4: Detect and Mitigate Bias – Medium 

                              4. Mitigating Bias In AI and Ensuring Responsible AI 

                              5. Addressing bias and privacy challenges when using AI in HR 

                              An AI image of a bunny dressed like a Beefeater

                              How technology can create and combat synthetic media 

                              What are Deep Fakes? 

                              Deep Fakes are a type of synthetic media that uses artificial intelligence (AI) to manipulate or generate audio, video, or images. They can create realistic-looking content that appears to show people doing or saying things that they never did or said. For example, a Deep Fake video could show a politician making a controversial statement, a celebrity endorsing a product, or a person’s face swapped with another person’s face. 

                              Below are examples of one with an Arnold Schwarzenegger Deep Fake starring in James Cameron’s Titanic

                               

                              How do Deep Fakes work? 

                              Deep Fakes are created by using deep learning, a branch of AI that involves training neural networks on large amounts of data. Neural networks are mathematical models that can learn patterns and features from the data and apply them to new inputs. There are different methods to create Deep Fakes, but one of the most common ones is called generative adversarial networks (GANs). 

                              GANs consist of two neural networks: a generator and a discriminator. The generator tries to create fake content that looks like real content, while the discriminator tries to distinguish between the real and the fake content. The two networks compete, improving their skills over time. The result is fake content that can fool both humans and machines. 

                              What are the threats of Deep Fakes? 

                              Deep Fakes pose several threats to individuals, organizations, and society. Some of the potential harms of Deep Fakes are: 

                              • Disinformation and propaganda: Deep Fakes can be used to spread false or misleading information, influence public opinion, undermine trust in institutions, and incite violence or conflict. 
                              • Identity theft and fraud: Deep Fakes can be used to impersonate someone’s voice, face, or biometric data, and gain access to their personal or financial information, accounts, or devices. 
                              • Blackmail and extortion: Deep Fakes can be used to create compromising or embarrassing content that can be used to coerce or threaten someone. 
                              • Privacy and consent violation: Deep Fakes can be used to create non-consensual or invasive content that can harm someone’s reputation, dignity, or mental health. 

                               An example of how close a Deep Fake can be to the original

                              The people below were generated by the website www.thispersondoesnotexist.com such images can be used in fake social media accounts.

                              How are companies dealing with Deep Fakes? 

                              While Deep Fakes pose a serious challenge, they also offer an opportunity for innovation and collaboration. Many companies are developing tools and solutions to detect, prevent, and mitigate the impact of Deep Fakes. Some of the examples are: 

                              • Adobe: Adobe has created a tool called Content Authenticity Initiative (CAI) that aims to provide a secure and verifiable way to attribute the origin and history of digital content. CAI uses cryptography and blockchain to create a tamper-proof record of who created, edited, or shared the content and allows users to verify the authenticity and integrity of the content. 
                              • Meta: Meta, formerly known as Facebook, has launched a program called Deep Fake Detection Challenge (DFDC) that aims to accelerate the development of Deep Fake detection technologies. DFDC is a global competition that invites researchers and developers to create and test algorithms that can detect Deep Fakes in videos. DFDC also provides a large and diverse dataset of real and fake videos for training and testing purposes. 
                              • Microsoft: Microsoft has developed a tool called Video Authenticator that can analyze videos and images and provide a confidence score of how likely they are to be manipulated. Video Authenticator uses a machine learning model that is trained on a large dataset of real and fake videos, and can detect subtle cues such as fading, blurring, or inconsistent lighting that indicate manipulation. Microsoft also provides a browser extension that can apply the same technology to online content. 
                              • X (formerly known as Twitter): X/Twitter has implemented a policy that requires users to label synthetic or manipulated media that are shared on its platform. The policy also states that X/Twitter may remove or flag such media if they are likely to cause harm or confusion. Twitter uses a combination of human review and automated systems to enforce the policy and provide context and warnings to users. 
                              • Deeptrace: Deeptrace is a startup that specializes in detecting and analyzing Deep Fakes and other forms of synthetic media. Deeptrace offers a range of products and services, such as Deeptrace API, Deeptrace Dashboard, and Deeptrace Intelligence, that can help clients identify, monitor, and respond to malicious or harmful uses of Deep Fakes. Deeptrace also publishes reports and insights on the trends and developments of synthetic media. 

                              These are just some of the examples of how companies are tackling the problem of Deep Fakes. There are also other initiatives and collaborations from academia, government, civil society, and media that are working to raise awareness, educate users, and promote ethical and responsible use of synthetic media.