Category Archives: Machine Learning
2023: A Year of Teachings and Change in the Tech World – Medium
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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2023: A Year of Teachings and Change in the Tech World - Medium
Demystifying AI in Education. As artificial intelligence extends its | by Colin Cooper | Dec, 2023 – Medium
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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Demystifying AI in Education. As artificial intelligence extends its | by Colin Cooper | Dec, 2023 - Medium
AI/ML trends for 2024: what happens after the hype? – DatacenterDynamics
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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AI/ML trends for 2024: what happens after the hype? - DatacenterDynamics
Financial Services Explores GenAI in Interesting Ways – Financial Services Explores GenAI in Interesting Ways – InformationWeek
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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Financial Services Explores GenAI in Interesting Ways - Financial Services Explores GenAI in Interesting Ways - InformationWeek
The AI Revolution in Mobile Apps: A Deep Dive into Proactive Intelligence – Medium
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
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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Revolutionizing Retail: Approach of Maneesha Bhalla in Advanced Analytics and AI - CIO Look - CIO Look
The Potential of AI in Cancer Care: Revolutionizing Cancer Treatment – Medriva
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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The Potential of AI in Cancer Care: Revolutionizing Cancer Treatment - Medriva
Using geospatial data & machine learning to predict water quality in Ethiopia – World Bank
Simone D. McCourtie / The World Bank
Expanding access to safe drinking water in low and middle-income countries is a key human development priority, with targets set at national and global levels. As such, ensuring access to safe water, sanitation and hygiene for all is also one of the Sustainable Development Goals.
Information on drinking water quality is key to monitoring progress towards achieving global and national targets. Therefore, it is important to accurately measure quality of drinking water accessed by households and individuals and to determine if drinking water is free from biological and chemical contamination.
An increasing number of countries (over 50 to date) have now integrated objective water quality testing in national household surveys to monitor access to safely managed drinking water services. This approach enables the collection of representative information for general household populations, with the potential to disaggregate results by different geographic and socioeconomic groups.
The ability to link water quality information to the wealth of information collected in household surveys facilitates research, as well as the identification of effective interventions to improve access to safely managed drinking water services.
However, integrating water quality testing in household surveys requires additional financial resources and specialized technical assistance, and can increase burden on statistical agencies, especially in resource-constrained contexts. For example, E. coli testing in the field requires equipment, consumables and dedicated training for field staff on aseptic techniques, incubation and interpreting results.
In our recent study, Addressing gaps in data on drinking water quality through data integration and machine learning: evidence from Ethiopiaa collaborative work between the World Bank Living Standards Measurement Study (LSMS) team and the Joint Monitoring Programme (JMP) of the World Health Organization and UNICEFwe proposed an approach to fill data gaps in drinking water quality.
Lets unwrap our methodology step by step.
The idea is that while it may not be logistically and financially possible to implement water quality testing in every household survey, data obtained from a recent survey can be integrated with publicly available geospatial data on rainfall, temperature, proximity to the nearest market and roads, among others, and in turn used to train a machine learning model to generate reliable insights on drinking water quality in years when no surveys were conducting tests on the ground.
The country selected as a case study for our research was Ethiopia. In 2016, when the latest data on water quality was collected as part of the Ethiopian Socioeconomic Survey, about 68 percent of households had access to drinking water from improved sources, such as piped sources, protected wells and springs. However, over half of those improved sources were contaminated (Figure 1).
Using water quality testing data from the third wave of the Ethiopia Socioeconomic Survey (ESS3) in 2016, our study, Addressing gaps in data on drinking water quality through data integration and machine learning: evidence from Ethiopia, examined the performance of a range of commonly used machine learning algorithms to predict E. coli contamination in the households drinking water sources.
The study developed a predictive model for contamination of drinking water sources by integrating socioeconomic survey data with geospatial data sources on the basis of household GPS locations. It compared a few commonly used classification algorithms including GLM, GLMNET, KNN, Support Vector Machine, and two decision tree-based classifiers: Random Forest (RF), and XGBoost. RF performed the best across most metrics, with XGBoost becoming a close runner up.
The study also examined the performance of different groups of predictors variables, namely household demographic and socioeconomic attributes, water service particularities and geospatial variables, on the performance of the algorithms and applied the predictive models to other waves of the ESS, in 2013/14 and 2018/19.
Overall, predictions for ESS3 (2015/16 ESS) were comparable to the actual data under different scenarios. The study finds that a model that has all prospective predictor variables is found to have a strong discrimination ability (Area under the curve (AUC) 0.91; 95% Confidence Interval (CI) 0.89, 0.94). Model performance was poor when type of water source was the only predictor (AUC 0.80; 95% CI 0.77, 0.84).
However, augmenting water source variables with selected household-level socioeconomic predictors, but excluding geospatial variables, resulted in a performance comparable to the full model (AUC 0.89; 95% CI 0.86, 0.91).
The model with only geospatial predictor also achieved a performance that was comparable to the full model (AUC 0.91; 95% CI 0.88, 0.93). The geospatial variables are also key predictors of contamination in the full predictive model (Figure 2).
Overall, three key take-away messages emerge from our study:
Machine learning approaches can be used to develop a model and fill the gap that might arise due to the challenges of implementing a water quality testing.
A georeferenced household survey with objective water testing and basic data on socioeconomic attributes, integrated with geospatial data sources, can be used to develop reliable predictive models for drinking water quality.
Provided that the data from a recent survey with objective water quality testing exist, predictive machine learning models relying exclusively on geospatial variables may also suffice for understanding variations in risk of E. coli contamination in drinking water sources and generating water quality risk maps.
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Using geospatial data & machine learning to predict water quality in Ethiopia - World Bank
Using deep learning to identify teens most in need of mental health support – Medical Xpress
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The personal yet global struggle with mental health may be more visible now than ever before. Yet many people still find it difficult to access the support they need.
In Japan, suicide is sadly the leading cause of death for young people. Researchers, including from the University of Tokyo, have carried out a six-year study to better understand the myriad factors which can impact adolescent mental health. After surveying 2,344 adolescents and their caregivers, and using computer-based deep learning to process the results, they were able to identify five categories into which the young people could be grouped.
Nearly 40% of those involved were classified as groups with some problems. Of these, almost 10% lived with mental health problems that had not been identified by their caregivers. This group was most at risk of self-harm and suicidal ideation. Identifying the factors that may lead young people to suicide and who is most at risk is key to supporting preventive efforts and early intervention.
Last year in Japan, 514 youths and children aged 18 and younger tragically lost their lives to suicide. This was the highest number for this age group since records began in 1978. Suicide is the leading cause of death for people aged 15 to 34 years old, according to data from Japan's Ministry of Health, Labor and Welfare. While adult suicide rates have been generally declining over the past 10 to 15 years, the reverse has been noted for adolescents. Officials speculate that school-related issues, difficult personal and family relationships, and lingering impacts of the pandemic may have contributed to the high number of deaths.
The World Health Organization (WHO) identifies suicide as a major global public health concern, but also says it is preventable through evidence-based interventions and by addressing factors that can lead to poor mental health. Researchers from the University of Tokyo and the Tokyo Metropolitan Institute of Medical Science are analyzing data on various problems in adolescence which were assessed both by self and caregivers, resulting in identification of young people who may be at suicide-related risk.
"We recently found that adolescents who were considered to have no problems by their caregivers actually had the highest suicide-related risk," said Daiki Nagaoka, a doctoral student in the Department of Neuropsychiatry at the University of Tokyo and a hospital psychiatrist. "So it is important that society as a whole, rather than solely relying on caregivers, takes an active role in recognizing and supporting adolescents who have difficulty in seeking help and whose distress is often overlooked."
The team surveyed adolescents and their caregivers in Tokyo over a period of six years. The participants completed self-report questionnaires, answering questions on psychological and behavioral problems such as depression, anxiety, self-harm and inattention, as well as their feelings about family and school life. The team also made note of factors such as maternal health during pregnancy, involvement in bullying and the caregivers' psychological states.
Now published in The Lancet Regional HealthWestern Pacific, the study began when the children were 10 years old, and checked in with them again at ages 12, 14 and 16. Overall, 3,171 adolescents took part, with 2,344 pairs of adolescents and their caregivers participating throughout the full study.
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"Psychiatry faces challenges in understanding adolescent psychopathology, which is diverse and dynamic. Previous studies typically classified adolescents' psychopathological development based on the trajectories of only two or three indicators. By contrast, our approach enabled the classification of adolescents based on a number of symptom trajectories simultaneously by employing deep-learning techniques which facilitated a more comprehensive understanding," explained Nagaoka.
Deep learning, a computer program that mimics the learning process of our brains, enabled the team to analyze the large amounts of data they collected to find patterns in the responses. By grouping the trajectories of the psychological and behavioral problems identified in the survey, they could classify the adolescents into five groups, which they named based on their key characteristic: unaffected, internalizing, discrepant, externalizing and severe.
The largest group, at 60.5% of the 2,344 adolescents, was made up of young people who were classified as "unaffected" by suicidal behavior.
The remaining 40% were found to be negatively affected in some way. The "internalizing" group (16.2%) persistently internalized problems and showed depressive symptoms, anxiety and withdrawal. The "discrepant" group (9.9%) experienced depressive symptoms and "psychotic-like experiences," but had not been recognized as having such problems by their caregivers. The "externalizing" group (9.6%) displayed hyperactivity, inattention and/or behavioral issues but few other problems.
Finally, the smallest group was categorized as "severe" (3.9%) and dealt with chronic difficulties of which their caregivers were aware, in particular psychotic-like experiences and obsessive-compulsive behavior.
Of all the groups, young people in the "discrepant" category were at highest risk of self-harm and suicidal thoughts. The researchers found that they could significantly predict who would be included in this group based on whether the child avoided seeking help for depression, and whether their caregiver also had a mental health problem.
The researchers suggest that the caregiver's mental state could impact the adolescent's mental health through both genetic factors and parenting environment, such as the caregiver's ability to pay attention to the difficulties an adolescent might face. Although this research has several limitations, it still enabled the team to identify a number of risk factors that could be used to predict which groups adolescents might fall into.
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"In daily practice as a psychiatrist, I observed that existing diagnostic criteria often did not adequately address the diverse and fluid difficulties experienced by adolescents," said Nagaoka. "We aimed to better understand these difficulties so that appropriate support can be provided. Next we want to better understand how adolescents' psychopathological problems interact and change with the people and environment around them. Recognizing that numerous adolescents face challenges and serious issues, yet hesitate to seek help, we must establish supportive systems and structures as a society."
More information: Identify adolescents' help-seeking intention on suicide through self- and caregiver's assessments of psychobehavioral problems: deep clustering of the Tokyo TEEN Cohort study., The Lancet Regional HealthWestern Pacific (2023). DOI: 10.1016/j.lanwpc.2023.100979
If you or someone you know is struggling, free help and support is available. For a list of helplines around the world, please visit: http://www.suicide.org/international-suicide-hotlines.html
Befrienders International provides confidential support to people in emotional distress or crisis: https://www.befrienders.org
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Using deep learning to identify teens most in need of mental health support - Medical Xpress
Featureform Raises $5.5M by Refining and Accelerating the Way Teams Work on AI and ML – Datanami
SAN FRANCISCO, Dec. 15, 2023 Featureform has announced $5.5 million in seed funding led by GreatPoint Ventures and Zetta Venture Partners with participation from Tuesday Capital and Alumni Ventures. This round of capital will allow Featureform to expand its product growth and increase support for existing and new enterprise customers, in addition to its open-source community. The completion of the Seed round brings Featureforms total funding to date to $8.1 million.
At enterprise companies, LLM usage has surged alongside traditional ML use cases. At the heart of both these systems is private data. The most critical thing that ML teams do is take their raw data and transform it into valuable signals to feed into LLMs via prompts or ML models as inputs. Featureform believes there needs to be a unified framework to define, manage, and deploy these signals (or features). This creates a unified resource library that can be used by all ML/AI teams across an organization with built-in search & discovery, monitoring, orchestration, and governance. Featureform has seen to this be true with their existing customers in the ML space and has begun spearheading this approach in the LLM space.
MLOps is moving out of the hype phase and entering the actual productivity phase, says Featureform Founder and CEO Simba Khadder. On the backend of this, were seeing a huge wave of new use-cases that have been unlocked with LLMs. Data is at the core of these two systems, and in practice, the problems look almost identical. Featureforms frameworks will fundamentally change the way ML and AI teams work with data.
The rise of Retrieval Augmented Generation architecture, or RAG, has provided a way for data scientists to inject relevant signals and content from their data sets into their prompts as content to increase an LLMs accuracy and decrease likelihood of hallucination. These signals are analogous to traditional machine learning features that youd find in a feature store. The big difference is that, after being processed, they are stored in a vector database. By adding vector database support, Featureform becomes the hub where data scientists can define, manage, and deploy their features for both ML and LLM systems.
Featureforms feature store platform offers a distinct advantage in the market with its unique virtual architecture, says Gautam Krishnamurthi, Partner at GreatPoint Ventures. This virtual approach not only sets them apart from the competition, but also significantly lowers the cost of feature store implementation in the large and growing MLOps market. Coupled with their expert team, Featureform provides a best-in-class solution in the market for building out machine learning feature management. We are excited to support the Featureform team in their Seed round and beyond!
Featureform provides data scientists with a framework to turn their data into useful features for ML models and LLMs. By using Featureform, these teams:
To learn more visit https://featureform.com.
About Featureform
Featureform is the creator of the virtual feature store. Our mission is to streamline how data and model features are built and maintained in machine learning orgs. Our python framework and feature store does away with copy and pasting between scattered notebooks with names like Untitled18.ipynb, unifies feature pipelines between experimentation and production, deduplicates repeated features across teams, and eliminates ambiguously named tables like feature_table_v5. While we pride ourselves on our open-core model, we also offer a robust enterprise solution with governance, streaming, and more. We are proudly based out of San Francisco.
Source: Featureform
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Featureform Raises $5.5M by Refining and Accelerating the Way Teams Work on AI and ML - Datanami