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2023: A Year of Teachings and Change in the Tech World – Medium

December 31, 2023 | Author: admin

As 2023 draws to a close, it stands out as a pivotal year in the technology sector, marked by significant advancements and paradigm shifts. This year has not only witnessed groundbreaking innovations but also reshaped our interaction with technology, paving the way for a future that promises both connectivity and complexity.

The evolution of AI and machine learning has been a central narrative of 2023. Weve seen AI models grow in sophistication, particularly in natural language processing, as evidenced by OpenAIs advancements, Googles AI research, and DeepMinds innovations. These developments have broadened AIs applications in diverse fields, but theyve also intensified debates around AI ethics, as discussed in MIT Technology Review, raised concerns about job displacement highlighted by the World Economic Forum, and sparked discussions about algorithmic bias, a topic extensively covered by Nature.

Quantum computing has transitioned from theoretical exploration to tangible progress this year. Major strides by IBMs quantum team, Googles Quantum AI lab, and startups like Rigetti have accelerated this race. The potential of quantum computing to revolutionize fields like cryptography, as analyzed by the IEEE Spectrum, materials science, and complex system modeling, is immense. However, it also challenges current cybersecurity norms, a concern raised by reports from the National Institute of Standards and Technology (NIST).

The expansion of 5G networks, significantly covered by GSMA, has been transformative in 2023. This enhanced connectivity has improved remote work technologies, as reported by Gartner, and facilitated IoT advancements, including the development of smart cities, a trend highlighted by the Smart Cities Council. While this promises a more interconnected world, it also raises issues of digital divides, as discussed by the United Nations, and concerns over data privacy, as reported by Privacy International.

Blockchain technology, beyond its cryptocurrency roots, has shown diverse applications this year. Its potential in supply chain management, as explored by Harvard Business Review, in creating transparent systems for voting, as discussed by the Stanford Social Innovation Review, and in other sectors, has been notable. However, challenges in scalability and energy consumption, highlighted by research from the University of Cambridge, and regulatory concerns, as discussed by the Journal of International Banking Law and Regulation, persist.

Cybersecurity has remained a critical issue in 2023. High-profile ransomware attacks, as reported by Cybersecurity Ventures, data breaches affecting millions, as covered by Infosecurity Magazine, and the evolving nature of cyber threats, as analyzed by Kaspersky, underscore the need for robust cybersecurity measures. These incidents emphasize the importance of innovation in this field, a topic extensively covered by the International Journal of Information Security.

A significant shift towards sustainability in the tech industry has been a key feature of 2023. Initiatives in green computing, as reported by the Green Electronics Council, efforts to reduce e-waste, highlighted by the United Nations Environment Programme, and the development of energy-efficient technologies, as discussed by the International Energy Agency, reflect a growing commitment to environmental responsibility in the tech sector.

The lessons and transformations of 2023 set the stage for 2024. The coming year is poised to build on these foundations, as we navigate the complexities of an increasingly tech-driven world. The challenges ahead, from ethical AI deployment to bridging digital divides, are significant, but so are the opportunities for innovation and progress.

In sum, 2023 has been a year of both teachings and change. It has underscored the immense potential of technology to drive progress, but also the need for cautious and responsible advancement. As we step into 2024, the journey of technological evolution continues to be one of the most compelling narratives of our time.

Happy New Year! Heres to 2024, a year where we not only continue to learn but also excel in implementing our newfound knowledge.

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Demystifying AI in Education. As artificial intelligence extends its | by Colin Cooper | Dec, 2023 – Medium

December 31, 2023 | Author: admin

As artificial intelligence extends its reach into more corners of academics and administration, some educators are expressing unease over tech systems influencing student experiences without transparency. Unlike comparatively traditional programs with visible logic flows, the inner workings of many machine learning models remain mostly inscrutable. When paired with automation determining personalised learning trajectories or predicting at-risk students, this cloak of mystery risks undermining stakeholder trust in AI. So, how can education leaders proactively foster market confidence and still take advantage of the potential power AI provides?

The first step is to adopt a consistent voice that speaks clearly to stakeholders. Making sure that your messaging is both transparent and accessible helps build trust around how AI is being used in education. Use keywords like integrity, privacy, ethics, or transparency when discussing AI so teachers, students and parents know all data will be treated with respect. This also serves to reassure those concerned about automated decision-making because humans in positions of authority can still intervene if needed.

By keeping transparency at the core of communication, educational leaders can ensure that everyone understands how AI systems are powering educational initiatives and that they are being used responsibly and ethically. This will go a long way to ensure that AI in education remains safe and equitable for all participants and helps demystify the wizard behind the curtain.

Adopting a commitment to maintaining strong human checks against potential algorithm harm will be essential for schools. Teachers should continually audit system recommendations for fairness and relevance, and data professionals sitting alongside pedagogy experts should regularly evaluate if the gathered metrics truly and accurately assess desired outcomes. Establishing these checks and balances workflows upfront should help prevent reactionary course corrections later.

Accessible interfaces visualising what information feeds AI systems will also promote transparent decision-making. All stakeholders should be empowered to contribute their input and opinions on how AI is used in the classroom, providing a window into the magicians hat of algorithms. This can help ensure AI applications are truly beneficial and equitable for all students regardless of race, gender or socio-economic status.

Overall, responsible development of AI technology requires an iterative process where educators make decisions on behalf of learners while considering ethical principles, including safety, fairness, privacy and accountability. By cultivating a culture that puts these values first, educational institutions will be better equipped to reap the benefits of AI making classrooms smarter places to learn.

By investing in technology that leverages artificial intelligence (AI) for personalised learning, schools can gain insights into whats working for students, as well as uncover areas for improvement. AI-enabled platforms provide data that can give educators a more comprehensive view of student performance, open-ended feedback, and powerful predictive analytics. By leveraging AI-driven solutions, schools will be able to offer a more personalised learning experience for each student, better-connecting educators with the resources they need to effectively support learners.

Additionally, AI technology allows teachers to identify at-risk students quickly and efficiently, providing them with extra attention when needed. This can help create an environment where all learners are well-supported and successful. Ultimately, investing in AI technology can make classrooms smarter creating opportunities for greater student engagement, more effective teaching strategies, and improved overall learning outcomes.

At its core, AI should be used first and foremost to make learning more effective and equitable for all not just those with the most resources or access. By investing in technology that puts people first while utilising the power of AI, educators can provide students with the right kind of learning experience. AI can provide teachers with the insights they need to personalise instruction, identify potential learning gaps, and make smarter decisions when it comes to classroom management. It can also make tedious tasks like grading easier and more accurate, freeing up valuable time that can be spent on instruction and helping students learn effectively.

With AI in the classroom, teachers have access to a powerful tool for positive change in education one that has the potential to revolutionise how we teach, learn, and measure student success. Lets embrace this technology for what it is a powerful force for good that can be used to create more equitable classrooms. By putting people first and investing in AI solutions tailored to each unique learner, we can ensure success for all.

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AI/ML trends for 2024: what happens after the hype? – DatacenterDynamics

December 31, 2023 | Author: admin

Artificial intelligence (AI) and machine learning (ML) based language processing models were the hot topics of 2023. Boosted by the novelty of tools like ChatGPT, Midjourney, Soundful, and others, the public hype made it feel as if AI is radically changing everything we do.

However, at the end of the year, there are increasingly more signs that we are not quite there yet. Limitations of these tools, as well as legal and ethical challenges to their development, leave less to celebrate and more to do before another breakthrough in AI adoption.

According to McKinsey's The State of AI 2022 report, AI adoption has settled between 50 percent and 60 percent over recent years and even went slightly down since 2019. Around the same time, multiple rollouts of tools based on generative AI made it seem that, on the contrary, we are experiencing a giant surge in AI adoption in business.

However, the scale of it was probably blown out of proportion. Companies like Salesforce and Microsoft race to be the world's first to introduce generative AI tools for particular tasks like summarizing customer data and generating real-time tips for meetings. Yet, even among businesses with $1 billion in yearly revenue, 60 percent are still a year or two away from implementing their first generative AI solution.

As long as the novelty and hype worked for good publicity, there was a reason to implement AI tools without expecting a prompt return on investment. That time has passed. In today's economic conditions, boards and investors will increasingly demand proof of positive results when authorizing AI adoption. So far, it seems that, at their current stage of development, the value generative AI tools can produce is limited.

Analysts from CSS Insight and Gartner find generative AI "overhyped" and predict that it will fade away from public interest already in 2024. Before AI can live up to this year's hype, we need to address the lack of reliability and accuracy that comes with superficially generating results according to statistical probability.

On the other hand, generative AI is just part of AI research. Moving forward, we might see the focus shifting from generative to causal AI and more nuanced machine learning techniques, such as federated learning.

While generative AI equates correlation with causation, advanced causal AI should function more like the human mind. It goes beyond statistics when examining the possible relationships between cause and effect. Thus, it can better discover what gives meaning to word sequences and produce more reliable results.

Federated machine learning is a framework in which ML algorithms can be trained without direct access to users' private data. In this decentralized paradigm, multiple partners with separate datasets train the algorithm collaboratively but without ever exchanging or pooling input data. This method can help solve the pressing issues of data privacy and isolated data islands.

This is essential technological innovation since accumulating legal cases regarding the privacy and ownership of data used to train AI already pose challenges to wider AI adoption. Courts and regulatory bodies agreeing on clear rules for further AI development and usage should also play an important part in addressing these challenges.

Of course, the Gen AI market is not going to roll back even if the general public will not watch it as enthusiastically as this year. The ML market is estimated to grow at 18.73% annually between 2023 and 2030, resulting in a market volume of $528 billion by 2030. We might even see new major players in the field of large language models (LLMs), providing training services and computing resources.

Gen AI is already making an impact on a number of industries, including marketing, design, and cybersecurity. The coming years might see it spreading into pharmaceutical, manufacturing, engineering, automotive, aerospace, and energy industries, maybe even streamlining core business processes.

The ability of businesses to adopt and deploy Gen AI further will depend on the providers' ability to serve these models as web-based APIs. Companies already implement ChatGPT into their daily tasks, such as customer care chatbots, generating leads, collecting product feedback, or summarizing video content. Learning the concept of causation and providing API access might allow Gen AI to be used in "harder" technical areas, like predictive maintenance.

To sum up, 2024 is going to be the year when we redefine the field of AI. After a long time of asking what AI could do, we are focusing more on what it should be enabled to do. Case law and national as well as intergovernmental institutions must provide some boundaries here.

Meanwhile, the market demand for quality over fast adoption should drive commercial AI developers to explore new areas. In all likelihood, Gen AI will not go away, but the field is going to be redefined by those striving for more intricate solutions.

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Financial Services Explores GenAI in Interesting Ways – Financial Services Explores GenAI in Interesting Ways – InformationWeek

December 31, 2023 | Author: admin

A surprising finding in our recent Generative AI Radar North America survey was that the highly regulated, risk-averse financial services business was among the leading adopters of generative AI: Thirty-two percent of participating financial services organizations had either implemented or were currently implementing, generative AI solutions, while 23% had established use cases that created business value. Usually slow to try new technologies, financial services companies seem to be making an exception with generative AI in anticipation of its impact throughout their business by enhancing user experience, improving content generation and creativity, increasing operational efficiency and automation, and streamlining product development and design.

Specifically, financial services and insurance companies in North America that participated in the survey saw potential for using generative AI to produce and personalize policy documents and customer communications, extract information from financial documents, generate synthetic data to train machine learning models to detect fraud, and assist in regulatory compliance, to name a few. But in addition to working on the usual suspects, financial services companies around the world are deploying generative AI in unexpected ways.

Related:The Evolving Ethics of AI: What Every Tech Leader Needs to Know

Here are a few examples:

For assessing creditworthiness and risk: Banks can use generative AI in core functions, such as credit scoring and risk management. In place of traditional scoring methods, they can use machine learning and generative AI to analyze vast and varied data from multiple sources to create a more holistic evaluation of a borrowers creditworthiness. Similarly, they can train generative AI on historical data to identify financial and other risks before they blow up.

For generating financial advice: Financial and investment advisory firms can train generative AI on proprietary customer data -- financial status and goals, risk profile, spending behavior etc. -- to generate recommendations on budgeting, trading and investing, managing risk etc. They can combine this with their human expertise to offer comprehensive and highly personalized advice to customers.

An example here is JP Morgan, which has developed a financial advisory tool called IndexGPT.

For product pricing and explanation: In the case of products where they have some pricing flexibility, financial services companies can use generative AI to understand customers willingness to pay and thereby charge the optimal price. Another interesting application is to use generative AI to compose easy to understand product descriptions and comparisons to help customers make the right selection.

Related:4 Ways AI Is Rocking This CISOs World

For improving financial behaviors: Why do people persist with injudicious financial behaviors, ignoring rational counsel? Well, it appears that emotions have a key role to play in how people react to advice. Generative AI could step in here to correct customers financial behaviors by appealing to their emotions. Simple applications of this kind already exist, with chatbots and apps using humor and encouragement to promote certain behaviors. These interactions can be improved by using generative AI to compose more detailed and meaningful responses. It is also possible that generative AI may assist human advisors in more involved interventions by gathering customer inputs and highlighting the emotional triggers that can be used to modify their behaviors.

According to one research report, the generative AI in financial services market will multiply nearly tenfold between 2023 and 2032 -- from $1,186 million to $11,220 million at a CAGR of 28.36%. While the industry could potentially benefit greatly from this technology, it must also be cognizant of the risks. Currently, generative AIs value mainly comes from its ability to create content based on large datasets containing text, code, images, and videos, at speed.For a knowledge, communication, and documentation-intensive business such as financial services, GenAIs natural language capabilities are particularly relevant. Banks and financial institutions can use the technology to summarize large documents, offer customer support, or draft new content at much lower costs than with manual effort. Not just that, they can also use GenAI tools to amplify their employees performance. Besides productivity and cost efficiency, the list of GenAI benefits includes simplified operations, better risk management and fraud detection, improvement in customers financial literacy and (financial) health, enhanced user experience, and faster, more accurate decisions.

Related:Google Enters GenAI Arms Race With Gemini

At the same time, the use of generative AI brings certain concerns and risks for financial institutions. Data challenges rank right at the top: If the dataset being used by the generative AI model is not of good quality, the outcome can have all types of flaws, including inaccuracy and bias (causing discriminatory credit decisions, for example); also generative AI algorithms can make mistakes, spread misinformation, and even hallucinate on occasion. Financial services organizations must also ensure ethical data usage, with full respect for security, privacy, confidentiality, and intellectual property rights by retaining a human in the loop to supervise the working of generative AI models. Also, as generative AI regulation evolves, very likely with regional differences, the highly regulated financial sector will face an even heavier compliance burden. The industry will also need to build the right talent by upskilling existing employees or hiring gen AI specialists; in addition, every employee will need to be trained on how to use the tools.

Financial services organizations are upbeat about the potential of generative AI to transform their business.While generative AI is growing at a rapid pace, its results may take time to show, since most companies will spend the next few years testing their models or piloting simple use cases, before progressing to large-scale initiatives.

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The AI Revolution in Mobile Apps: A Deep Dive into Proactive Intelligence – Medium

December 31, 2023 | Author: admin

The mobile app landscape is currently undergoing an unprecedented transformation, propelled by the integration of Artificial Intelligence (AI) and Machine Learning (ML). No longer relegated to mere information repositories or basic utility tools, mobile apps are evolving into intelligent companions. In this blog post, we will explore the profound impact of AI on mobile apps, delving into three key areas that go beyond personalization and redefine the user experience.

Introducing StartxLabs: Pioneers in Digital Services

Before we dive into the intricacies of the AI revolution in mobile apps, lets take a moment to acknowledge the driving force behind technological innovation. StartxLabs, a global website and mobile app development company, stands at the forefront, offering the finest in class digital services. Specialising in Cloud, DevOps, Digital Transformation, Technology Advisory, Identity and Access Management, IT Infrastructure, and Virtualization Services, StartxLabs has emerged as a trusted partner to various small, medium, and large organisations worldwide.

With clients spanning across Australia and the USA, StartxLabs brings a wealth of experience and expertise to the table. As we explore the transformative power of AI in mobile apps, its crucial to recognize the role of forward-thinking companies like StartxLabs in shaping the future of technology.

1. Predictive Analytics: A Shift from Reactive to Proactive Intelligence

Predictive analytics marks a paradigm shift in the realm of mobile apps. Imagine a fitness app that not only tracks your activities but anticipates your fatigue levels before you do. This app could utilise real-time biometrics to suggest lighter exercises or adjustments, transforming it from a reactive tool into a strategic partner in your fitness journey. Similarly, envision a financial app armed with predictive analytics that can foresee unexpected expenses and proactively adjust your budget to accommodate them. The true power of AI lies not just in analysing user behaviour but in learning from it, identifying patterns, and anticipating future needs. This proactive approach empowers users to make informed decisions, optimise their lives, and stay one step ahead.

To illustrate, consider scenarios where an AI-driven calendar app can predict busy periods and suggest time management strategies, or a productivity app that learns your work patterns to offer optimal task prioritisation.

2. Conversational AI: Beyond Bots to Nuanced Dialogue

The era of robotic, scripted chatbots is definitively behind us. Todays AI-powered conversational interfaces, driven by Natural Language Processing (NLP), engage in nuanced and context-aware dialogues. Picture a language learning app that not only understands your proficiency level but also recognizes your cultural background, tailoring its responses accordingly. This goes beyond mere grammar correction, offering culturally relevant insights that enhance the learning experience. In the realm of mental health apps, imagine a platform that can discern your tone and emotional state, providing personalised coping mechanisms tailored to your unique needs. These intelligent conversations foster a sense of connection and trust, transforming apps into accessible, supportive companions that transcend traditional interface limitations.

To delve deeper, consider the potential applications in customer service, where AI-driven chat interfaces can understand user frustration levels and respond with empathy, creating a more positive and effective interaction.

3. AI-powered Interfaces: Bridging the Digital and Physical Worlds

The AI revolution extends far beyond the digital realm, as Augmented Reality (AR) and computer vision blur the lines between screens and the physical world. Envision a travel app that overlays historical information on the cityscapes you explore, turning sightseeing into a captivating journey through time. Alternatively, picture a fitness app that analyses your workout form through your phones camera, providing real-time feedback and correcting your technique for improved performance and injury prevention. These AI-powered interactions make the digital world tangible, enriching experiences and expanding the possibilities of what mobile apps can offer.

Consider the implications in retail, where AI-driven AR applications can enable virtual try-ons, enhancing the online shopping experience by providing a realistic preview of products.

The Future is Intelligent: A Glimpse into Whats Next

The integration of AI in mobile apps is just the beginning. The future promises even more personalised and immersive experiences. Imagine smart homes that anticipate your every need, healthcare apps that diagnose illnesses before symptoms appear, or educational apps that tailor learning to your unique cognitive style. As AI continues to mature, mobile apps will become seamless extensions of ourselves, intricately woven into the fabric of our lives. They will anticipate our needs, support our goals, and contribute to a world that is smarter, smoother, and infinitely more personalised.

Embrace the Change: A Call to Action

To fully appreciate the transformative power of AI in mobile apps, its essential to open your phone with fresh eyes. Look beyond the pixels and buttons, and witness the invisible hand of AI shaping your experience. What may seem like just an app is, in fact, a glimpse into a future where technology becomes your intelligent companion. Every tap, swipe, and interaction becomes a step towards a better, more empowered you.

Conclusion

The AI revolution in mobile apps, guided by the innovative strides of companies like StartxLabs, is propelling us into a future where technology becomes an intuitive and personalised companion. The seamless integration of predictive analytics, conversational AI, and AI-powered interfaces is transforming our mobile devices into strategic partners that anticipate our needs, support our goals, and contribute to a world that is smarter and more personalised than ever before. As we witness the invisible hand of AI shaping our mobile experiences, let us open our devices with fresh eyes, embracing the change that turns every tap, swipe, and interaction into a step towards a better, more empowered version of ourselves. The journey towards a future where our devices become indispensable allies is underway, and with companies like StartxLabs at the forefront, the possibilities are boundless.

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Revolutionizing Retail: Approach of Maneesha Bhalla in Advanced Analytics and AI – CIO Look – CIO Look

December 31, 2023 | Author: admin

Contributing significantly to the field of advanced analytics within the retail sector, Maneesha Bhalla is the VP of Enterprise Data Analytics & AI at Designer Brands and a distinguished thought leader and TEDx speaker. With a rich background spanning over 20 years, Maneesha has become a trailblazer in the application of analytics to drive strategic priorities, revenue growth, and profitability.

Her leadership role at Office Depot involved spearheading the advanced analytics team, focusing on delivering actionable insights and leveraging machine learning models. Maneesha is at the forefront of utilizing analytics in marketing, particularly in the field of personalized marketing, showcasing her expertise in harnessing data to enhance customer experiences and drive business success.

Maneeshas journey as a data leader has been dynamic, starting with a foundation in data analysis and statistics during academic years in Chemical Engineering. The journey progressed through roles at data consulting firms, emphasizing data quality. Transitioning to Target Corp, Maneesha deepened expertise in merchandise planning, supply chain optimization, and inventory management as Senior Manager in Merchandising Analytics.

In the current role as the leader of the Data and AI Centre of Excellence at Designer Brands, Maneesha combines her past experiences to build data strategy for the enterprise. She oversees data engineering/integrations, cloud data engineering, business intelligence, and AI/ML. This role allows her to leverage her careers breadth and lead talented teams, striving to enable data-driven decision-making in the organization.

Blending Passion, Learning, and Transformative Impact

Maneeshas fascination with the power of data led her to start as a business analyst, using data analytics and visualization tools to generate insights. Wanting to explore beyond descriptive analytics, she delved into the potential of Artificial Intelligence (AI) and Machine Learning (ML) to create predictive and prescriptive solutions. Believing in the future impact of AI/ML on innovation and transformation and to keep up with latest in this field, she pursued learning through online courses and certifications and completed her second masters in Analytics from Georgia tech recently.

This journey into the field of AI/ML allowed Maneesha to work on diverse and complex problems, including predicting customer behavior, optimizing operations, and developing data strategies. As a leader in analytics and AI, she has led and mentored teams, fostering a culture of customer centricity, innovation, and results. Passionate about AI and ML, Maneesha looks forward to continuing her career in this field, always eager to learn new skills and techniques to improve performance and deliver value to stakeholders.

Elevating Customer Engagement

In Maneeshas role at DBI, she led a project to create a customer 360 data layer, centralizing and aggregating customer data from multiple sources for analytics and AI/ML workloads. Her team developed user-based product recommendation algorithms, resulting in increased customer engagement and retention. Tests showed an 11% increase in overall click-through rate (CTR) and a 9% increase in total recommended style demand per recipient. She also worked on a visual search proof of concept (POC) for the mobile app, meant for customers to use images for item searches.

Exploring applications of generative AI, Maneesha aims to enhance customer experiences and improve efficiency. She is investigating language-based models for personalized customer experiences and considering applications in marketing campaign content generation, product attribution enhancement, and automated product description generation.

Maneesha is also actively involved in educating leadership and peers on the capabilities and possibilities of AI/ML and generative AI. Her team has created training materials and best practice documents for running AI/ML workloads in GCP and using generative AI studio. Generating awareness and inspiring leaders on the art of the possible has been a key impact of her work, steering several initiatives and demonstrating value for the organization.

AI in Retail

Maneesha envisions the transformative role of AI in the retail industry, anticipating several key developments:

Maneesha sees AI as a pivotal force in driving innovation and transforming the retail industry. While it promises substantial benefits in terms of enhanced customer experiences and operational efficiency, retailers must also prioritize ethical considerations to build trust and ensure the sustainable and responsible use of AI technologies.

Upholding Transparency, Fairness, Privacy, and Security

The AI council at DBI plays a crucial role in fostering a responsible and ethical approach to AI adoption. The council is committed to principles and best practices that prioritize transparency, fairness, customer privacy, and security:

By upholding these principles, the AI council at DBI establishes a framework that not only accelerates AI adoption but also ensures responsible and ethical practices in alignment with industry standards and legal requirements.

Personalized Customer Experiences in the Data Landscape

Maneeshas passion for the field of Data and Analytics is evident, and her vision for the future reflects a commitment to leveraging AI for the betterment of the community. She envisions contributing to the broader domain of data and AI by partnering with other firms and leaders to advocate for the ethical and safe use of AI. Her goal is to bring AI into everyday life, enhancing the quality of life for individuals, especially for the elderly and children. While she acknowledges the potential of AI in robotics, she sees it as a tool to complement human efforts rather than replacing them entirely.

At Designer Brands, Maneesha plans to harness generative AI and machine learning to personalize customer touchpoints and enhance efficiency across various functions such as supply chain, marketing, merchandising, and IT. The focus on foundational data is emphasized, recognizing its crucial role in the optimal functioning of AI applications. Investments in data foundation, including tooling, systems, and the semantic layer, are part of the strategy to ensure clean, curated, and accurate data.

The approach to leverage pre-trained models available in existing cloud platforms for fine-tuning on company-specific data reflects Maneeshas forward-thinking perspective on the speed and scalability of AI implementation. This approach not only streamlines the integration of AI models but also sets the stage for transformative changes in how organizations can harness the power of AI, particularly in language-based applications.

Insights and Advice for Aspiring Professionals

Maneesha provides valuable advice to AI professionals and researchers aspiring to enter the dynamic technology sector. Here are key takeaways:

This advice reflects Maneeshas understanding of the multifaceted nature of AI and emphasizes not only technical proficiency but also ethical considerations and collaborative engagement within the AI community.

Charting the Data Frontier

Maneeshas journey in the data and analytics space has been marked by a commitment to overcoming challenges and driving innovation. While the technical hurdles of advancing AI and machine learning are invigorating, Maneesha underscores the crucial nature of transforming business processes and mindsets. The transition to new technologies often encounters resistance, particularly when AI systems, viewed as black boxes, replace traditional decision-making based on experience. Maneesha advocates for managing these shifts by offering comprehensive training and reskilling programs.

Additionally, she emphasizes the necessity of staying abreast of regulatory changes, ensuring AI systems remain compliant. Lastly, Maneesha sheds light on the financial aspect, highlighting the importance of strategic planning to optimize costs associated with running AI systems and to achieve a positive return on investment. Through her experiences, Maneesha exemplifies the resilience and adaptability needed to navigate the complex landscape of data and analytics.

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The Potential of AI in Cancer Care: Revolutionizing Cancer Treatment – Medriva

December 31, 2023 | Author: admin

The integration of Artificial Intelligence (AI) in healthcare has been a topic of global interest, with its potential to revolutionize the landscape of cancer care. Through advanced algorithms and machine learning, AI can significantly improve the accuracy and outcomes in cancer treatment. Despite the challenges that need to be addressed, the future of AI-assisted healthcare is incredibly promising and is expected to enhance patient care significantly.

As reported by The Conversation, AI has the potential to transform healthcare by offering new ways to improve the prevention, diagnosis, treatment, and management of cancer. AI technology can accelerate the development of new treatments, assist doctors in making faster and more accurate diagnoses, and provide personalized treatment. It also has the capacity to assist in surgical procedures and monitor patients vital signs.

The journal Cancers showcases a collection of research focused on the application of AI and Machine Learning in Cancer Research. These applications range from cancer screening, automated pathology and diagnosis, prognosis prediction, treatment personalization, drug discovery, and automated treatment planning. This extensive range of applications indicates the transformative potential of AI in oncology.

A discussion on Onclive explores the potential of AI in oncology, including its impact on precision medicine, cancer screening, diagnosis, patient interactions, and matching patients with clinical trials. The conversation highlights the vision of incorporating AI tools into everyday practice, enabling personalized cancer treatment, and facilitating major breakthroughs in understanding cancer.

The Chicago Tribune reports on a groundbreaking AI model developed by Northwestern researchers to predict outcomes for breast cancer patients. This model analyzes both cancerous and noncancerous cells, offering a comprehensive prognosis that could lead to more personalized treatment plans and reduce unnecessary chemotherapy.

A report on IndiaAI presents an insightful journey of Prof Debarka Sengupta in the field of AI, focusing on its role in cancer treatment. His research involving big data algorithms in Single-cell genomics and early cancer detection underscores the notable advancements AI brings to cancer care, especially in rural India.

In conclusion, the potential of AI in revolutionizing cancer care is vast. While there are challenges to be addressed, the advancements in AI-assisted healthcare are promising a future where cancer treatment will be more accurate, personalized, and effective.

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Which Altcoin Will Hit $1 in January 2024: Cardano (ADA) or Ripple (XRP)? – Coinpedia Fintech News

December 31, 2023 | Author: admin

Cardano traded under the bearish influence for over 25 months and triggered a strong rebound at the beginning of Q4 2023. The extended squeeze that prevailed for a prolonged period may be the primary reason, as the bulls used their accumulated strength to lift the price by over 180%. Currently, the ADA price appears to be in a decisive phase where a bullish rebound may lay the foundation for a fresh upswing.

In the long term, Cardanos price appears to be on track within a parabolic curve. The crypto, in its previous rally, underwent a parabolic recovery and marked new highs above $3 in August 2021. Since then, it has been maintaining a bearish trend. However, the recent rebound has displayed enough strength, and the recovery plot has been validated. Therefore, the price is believed to maintain a healthy upswing along the curve to form a new ATH somewhere during the end of Q3 2024 or in the first few weeks of Q4.

The market participants appear to have lost some attention over the crypto, as a massive variation in the volume has made the XRP price quite volatile. This has surprisingly held the levels within a narrow range, preventing the bears from taking over the rally. Now that bulls are trying hard to re-dominate the rally, a bullish upswing could probably compel the price to break out of the decisive pattern.

The weekly chart of the XRP price suggests the bulls have held a larger dominance despite the interim bearish pressures. After trading within a symmetrical triangle for over 30 months, the price is consolidating firmly along the upper resistance of the triangle and above the 0.23 FIB level. The volatility is expected to be maintained within the prevailing levels and further witness a breakout. Therefore, the XRP price may continue to consolidate for another couple of months, beyond which a breakout may lead the price not only above $1 but close to its previous highs.

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Crypto prices retreat as consolidation sets in following altcoin surge – Kitco NEWS

December 31, 2023 | Author: admin

(Kitco News) - It was a day of consolidation for the broader cryptocurrency market on Thursday as Bitcoin slid below $43,000 while many of the altcoins that had seen large price increases over the past week or two corrected lower as traders booked profits.

Stocks were higher for the majority of the trading day as investors looked to finish 2023 with the same strength that has pushed the S&P and Dow to record highs in recent days. But traders ran out of steam into the close, resulting in a flat performance for the S&P and Nasdaq, while the Dow gained 0.14%.

Data provided by TradingView shows that after Bitcoin (BTC) bulls pushed its price to a daily high of $43,835 in the early hours on Thursday, bears took control of the price action and dropped the top crypto to a low of $42,265 in the afternoon before bulls managed to halt the slide. At the time of writing, Bitcoin trades at $42,600, a decrease of 1.75% on the 24-hour chart.

BTC/USD Chart by TradingView

According to former Goldman Sachs executive Raoul Pal, the gains seen so far in 2023 are but the start of a crypto bull market cycle that could last up to four years and see Bitcoin hit a high of $1 million by 2025.

Looking at a long-term chart of Bitcoins performance since 2013 that Pal said is a perfect logarithmic trend chart, he said that his firm thinks the business cycle peaks sometime at the end of 2025, and that would suggest a crazy sort of target that could get somewhere between half a million and a million dollars in Bitcoin.

Do I expect that? Probably not, but who the hell knows right? Pal said.

He went on to suggest that the current bull market cycle is similar to the 2016-2017 cycle which saw digital asset prices surge higher as the result of a large influx of liquidity, and said that with the launch of a spot BTC ETF on the horizon and the approaching Bitcoin halving in April, this bull market is still in its early stages and has a long way to run.

These cycles can be crazy and this one feels more like the 2016-17 cycle than it does the prior cycle. And that cycle didnt have a lot of central bank printing, not in the US, Pal said. But central bank balance sheets were rising. We saw a 20% growth in liquidity. And what happened was crypto absolutely exploded. I kind of feel like thats the case. I dont focus on the end target. I focus on the structure. But Im just showing you the magnitude of the opportunity. And were still at one standard deviation oversold. Its all to play for. Weve barely started.

Crypto trader Nestay also thinks the current cycle is similar to what was seen in 2015-2017, and coined the term golden cycle to describe Bitcoin bull markets.

So far, our current golden cycle is most similar to the '15-'17 one, he said. We started off with a crash, but it recovered fast, a re-accumulation period followed, and now we are heading into the golden rejection.

During the 2015-2017 cycle, the golden rejection took place near $800 in mid-late June and was followed by a pullback to the previous resistance at $480, which flipped to support. After that, BTCs price steadily climbed higher until it underwent a blow-off top in late 2017.

If we do reject [near $50,000], I expect us to S/R flip the re-accumulation range, Nestay said. Like each past golden cycle, a re-accumulation phase follows into the halving before the bull begins his run and sends us into new ATHs, he concluded.

In every Four Year Cycle, there is always a resistance that rejects price for 3 years (black), said market analyst Rekt Capital. But in a new Candle 4, this resistance is finally broken.

In this four-year cycle that resistance is $46,000, he said. In 2024, BTC will break $46,000 easily.

Altseason fades away

The majority of tokens in the top 200 traded in the red on Thursday as traders looked to decrease their exposure ahead of a potential market-wide correction lower.

Bitcoin Gold (BTG) was the biggest gainer with an increase of 24%, followed by a 23% gain for Bitcoin SV (BSV), and a 14% increase for Tellor (TRB). Decred (DCR) was the biggest loser with a decline of 18%, while Kadena (KDA) fell 15.5%, and Raydium (RAY) lost 12.8%.

The overall cryptocurrency market cap now stands at $1.67 trillion, and Bitcoins dominance rate is 50%.

Disclaimer:The views expressed in this article are those of the author and may not reflect those of Kitco Metals Inc. The author has made every effort to ensure accuracy of information provided; however, neither Kitco Metals Inc. nor the author can guarantee such accuracy. This article is strictly for informational purposes only. It is not a solicitation to make any exchange in commodities, securities or other financial instruments. Kitco Metals Inc. and the author of this article do not accept culpability for losses and/ or damages arising from the use of this publication.

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Altcoin season incoming: analyst – crypto.news

December 31, 2023 | Author: admin

A Crypto Banter YouTube channel analyst sees the crypto market cycle accelerating into an intense altcoin season soon.

In a YouTube video posted on Dec. 28, the analyst laid out his perspective on where he sees the market heading in the near future.

We are at the stage of a bull market party, the analyst stated, referring to the gains seen across crypto assets over the past few months. However, he believes there will be two major legs to this bull run the first taking us to new all-time highs for Bitcoin (BTC) and major altcoins, then a cooldown period, followed by a breakout above those all-time highs later this year.

The analyst sees altcoin season accelerating rapidly now, saying, Were getting to the point where things are speeding up fast. He attributes this to the slowing momentum in Bitcoin, allowing altcoins to catch up fast.

With Bitcoin dominance dropping and money flowing out from Bitcoin into altcoins, the analyst thinks the market will see the biggest alt surge to Bitcoin that weve seen in a very long time coming. His target is for Bitcoin dominance to fall to the 48-49% level in the near future.

Its a phase of when altcoins really rally up quickly, and then I do think theres a cool down going into the possible halving.

Rather than exiting positions completely, the analyst advises rotating profits into Bitcoin and stablecoins to weather any potential market correction while remaining invested for continued upside.

Dont be afraid to bank a whole bunch of cash, he suggests, noting there may be a four-to-six-week period of consolidation before the next leg higher.

Regarding key levels, the analyst is watching to see if Bitcoin can break through resistance around $46,000-$48,000. Meanwhile, he has a short-term target of $2,500 for Ethereum (ETH), beyond which he believes ETH could rapidly rally to $3,500.

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