Inter Miami CF and JPMorgan Chase announced on Tuesday a naming rights partnership for Inter Miami CF’s 21,500-seat home venue, located in Fort Lauderdale, Fla. The electric home of Inter Miami CF will now be Chase Stadium. The major, multiyear partnership sees JPMorgan Chase become an official ...

Inter Miami CF and JPMorgan Chase Announce Naming Rights Partnership; Introduce Chase Stadium | Inter Miami CF

The US Supreme Court turned away an appeal that might have upended the $1.4 trillion leveraged loan market, leaving intact a legal victory for JPMorgan Chase &amp; Co. and other banks.

JPMorgan’s Leveraged-Loan Victory Survives Supreme Court Appeal - Bloomberg

JPMorgan Chase &amp; Co.’s Marko Kolanovic, who drew attention for his gloomy stock-market calls through last year’s rally, is raising a risk that’s mostly gone out of vogue on Wall Street: a return of 1970s-style stagflation.

JPMorgan’s Kolanovic Bucks Consensus by Seeing Stagflation Risks - BNN Bloomberg

J.P. Morgan Payments was ranked as 2024 sole quality leader for Asian Large Corporate Cash Management, along with 16 total Greenwich Excellence awards.

Coalition Greenwich Quality Leader in Asia for 2024 | J.P. Morgan

Get the most recent info and news about JPMorgan Chase on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

JPMorgan Chase & Co. operates as a financial services company worldwide. It operates in four segments: Consumer & Community Banking (CCB), Corporate & Investment Bank (CIB), Commercial Banking (CB), and Asset & Wealth Management (AWM). The CCB segment offers deposit and investment products and services to consumers; lending, deposit, and cash management and payment solutions to small businesses; mortgage origination and servicing activities; residential mortgages and home equity loans; and credit card, payment processing, auto loan, and leasing services. The CIB segment provides investment banking products and services, including corporate strategy and structure advisory, and equity and debt markets capital-raising services, as well as loan origination and syndication; cash management and liquidity solutions; and cash securities and derivative instruments, risk management solutions, prime brokerage, and research. This segment also offers securities services, including custody, fund accounting and administration, and securities lending products for asset managers, insurance companies, and public and private investment funds. The CB segment provides financial solutions, including lending, treasury, investment banking, and asset management to small business, large and midsized corporations, local governments, and nonprofit clients; and commercial real estate banking services to investors, developers, and owners of multifamily, as well as to office, retail, industrial, and affordable housing properties. The AWM segment offers investment and wealth management services across equities, fixed income, alternatives, and money market fund asset classes; multi-asset investment management services; retirement products and services; and brokerage and banking services. The company also provides ATM, online and mobile, and telephone banking services. The company was founded in 1799 and is headquartered in New York, New York.

JPMorgan Chase

JPMC published a in 2021 highlighting their approach to . This article summarizes the problem statement, solution, and other key technical components of the paper* paper Entity Linking What is Entity Linking? It's the task of assigning a to ambiguous mentions of in a text. unique identity named entities Here, “ ” from the text is given a unique identity via a URL (the most common type of ) “ ”. Note that the type of URI used to identify the mentioned entity depends on the domain uniquely. For eg, Instead of a web address, we could have used ISBNs if we were to identify books from a text. Paris URI wikipedia.org/wiki/Paris JPMC was interested in : from to the entities stored in their internal knowledge base (stored as a Mapping mentions of financial institutions news articles Knowledge Graph) An example is shown below: There are two sub-problems that must be defined: : Recognition Extraction of mentions from financial news articles. JPMC has used for this. Spacy : Linking Choosing the correct entity from the internal Knowledge Graph to be linked to the extracted mentions in the previous step. The paper discusses this step. A pictorial representation of this is shown below: String Matching These approaches capture the “morphological” structure of the entity names. The team experimented with (a) Jaccard (b) Levenshtein (c) (also known as ) Ratcliff-Obershelp Gestalt-Pattern-Matching (d) **Jaro Winkler**(e) N-Gram Cosine Similarity The con of these approaches is that they focus just on the “ ” of the names and not the semantics. An example of a failure case would be the matching of “Lumier” and “Lumier”. Even though they are exactly the same, they refer to two different entities. syntactics 2. : Context Similarity Methods These methods take the contexts around mentions and entities to give a similarity score. The Context for “mention” is text to the left and right of the mention, whereas The Context for “entity” is all the data that's stored in the KG for this entity. Finally, Cosine similarity / Jaccard similarity can be used on top of the context vectors. 3. : ML Classification Naive Bayes, Logistic Regression, and SVM are trained on (mention, entity) pairs to find the ones that should be linked 4. Learn to Rank Methods (LTR) These models work in tandem with ML approaches, which might give us multiple (mention, entity) pairs as the solutions. LTR approaches just narrow down to the most probable solution. The idea is to capture both (the meaning that the or stands for) and (character composition of the name) between the names and use a contrastive loss function to train a model. Semantic distance mention entity Syntactic distance We will see below how both of these distances are calculated step by step. Step 1: Obtain embeddings for Entities and Mentions To come up with both of the distances, the authors have proposed to use embeddings for as well as in the KG. mentions entities To obtain , the authors have used a (shown below) embeddings Entity Triplet Loss function For each entity, they used 10 positive and 10 negative samples, making . 10 <entity, positive word, negative word> triplets Unlike embeddings, which they had precomputed, were trained using the on-the-go embeddings approach, where the embedding matrix is learned during the training itself. Entity the embeddings mention Step 2: Calculate Syntactic Distance score Before going further, it's worth mentioning which was introduced by Google in 2016. You can find their official blog . We won’t go into the details, but to give a summary, it's an architecture that has two components — The Wide component and the Deep component. “Wide & Deep” architecture here score calculation is done using the WIDE part, which consists of a Syntactic Distance Linear Siamese Network. The output of the siamese network is the vectors for both the and the which are then compared to find the Euclidean distance. entity mention, Step 3: Calculate Semantic Distance score score calculation is done using the DEEP part Semantic Distance is the pre-trained embedding for the “Apache Corp” that was calculated in Step 1. To obtain the embedding for the , its left and right context words are fed into a Bi-LSTM network that trains the embeddings. The embedding vectors of and the ( are then used to find the Euclidean distance: eₖ mention mention (Vₘ) entity Vₑ) Step 4: Compute Contrastive Loss Both Syntactic and Semantic distances are combined in a weighted fashion as follows: The contrastive loss function is then combined as follows: Contrastive Loss Function where Y is the ground truth value, where a value of 1 indicates that mention m and entity e are matched, 0 otherwise. Combining all the pieces, the final model framework is shown below: At the time of writing this paper, JPMC was still in the process of deploying the model, which, once done, will help support users across JPMC in discovering relevant and curated news that matters to their business. From the cost perspective, not all the mentions are fed through the JEL framework as that would be computationally expensive. JPMC has put another blocking layer to funnel out the mentions that share less than 2 bigrams with the entities from their internal KGs. Once again, is the paper link if you would like to read the full paper. here Also published here Thanks for reading !! Follow Intuitive Shorts (my Substack newsletter), to read quick and intuitive summaries of ML/NLP/DS concepts. You can also follow me on . Medium

Neural Entity Linking and How JPMorgan Chase Plans to Use it

Exploring Shadow AI's impact on business: risks, strategies, and the quest for a secure, innovative future. How will companies navigate this new frontier? In December 2023, Amazon unveiled its latest venture, Q, promising a safer alternative to consumer-focused chatbots like ChatGPT. However, the excitement was short-lived. Just three days after the announcement, Amazon Q was embroiled in controversy. Employees were alarmed over its inadequate security and privacy measures, revealing that Q fell short of Amazon's stringent corporate standards. Critics its tendency for "hallucinations" and leaking sensitive information, including AWS data center locations, unreleased product features, and internal discount programs. Amazon's engineers were forced into damage control mode, addressing critical issues tagged as a "sev 2" emergency to prevent future fallout. AI highlighted Around the same time, Samsung Electronics Co. grappled with its own AI-induced headache. Sensitive internal source code found its way onto ChatGPT, spotlighting glaring security vulnerabilities. The response was swift: a company-wide ban on generative AI tools was communicated through an internal memo. Samsung's decision underscored the difficulties of managing data on external AI platforms, such as Google Gemini and Microsoft Copilot, where control over data retrieval and deletion is elusive. This move mirrored the concerns of , who viewed these AI services as a digital . Despite the ban's impact on productivity, Samsung remained firm, opting to develop in-house AI solutions for translation, document summarization, and software development until a secure AI-using environment could be established. 65% of Samsung employees Trojan horse Apple, too, joined the fray, prohibiting its employees from using ChatGPT and similar AI-powered tools. The ban was partly fueled by the tools' affiliations with Microsoft, a direct competitor, stoking fears about Apple's sensitive data security. This trend wasn't exclusive to tech giants; financial behemoths like , Deutsche Bank, Wells Fargo, and others also limited AI chatbots' use, aiming to shield sensitive financial information from third-party eyes. JPMorgan Chase Yet, these restrictions inadvertently birthed a culture of "Shadow AI," where employees use their personal devices at work in their quest for efficiency and time-saving, highlighting a significant policy-practice gap in AI usage. Shadow AI: Unseen Threat Although concrete data is scarce, numerous individuals at companies with AI restrictions have to employing such workarounds — those are only the ones open about it! This Shadow AI usage is prevalent in many organizations, encouraging the use of AI in ways that contradict or violate company policies, thus becoming an activity employees feel compelled to conceal. confessed As I dug into this issue deeper, I found some recent studies confirming that despite about in the workplace, . Recent research by Dell that 91% of respondents have dabbled with generative AI in their lives in some capacity, with another 71% reporting they've specifically used it at work. numerous stories companies restricting genAI use employees don't seem to be using it any less indicates The study conducted by highlights a significant gap between AI adoption in the workplace and the formal policies governing its use in Australia and New Zealand. While 63% of employees in these regions utilize AI for various tasks, only 36% of organizations officially allow it. The survey reveals that AI is being applied for creating written content (51%), enhancing productivity (37%), automating repetitive tasks (37%), improving decision-making (29%), and customer service (20%). However, only 11% of organizations have a comprehensive policy for AI usage, and 21% have no intention to establish any. ISACA Moreover, the study by ISACA indicates a lack of AI-related training within organizations, with only 4% offering it to all staff and 57% not providing any training, even to those directly affected by AI technologies. This situation raises concerns similar to those regarding Shadow IT, where employees use IT resources without formal approval, potentially risking organizational security and governance. Navigating the New Frontier of Risk and Responsibility Much like how Shadow IT snuck up on businesses, Shadow AI is already present, forcing organizations to confront their GenAI stance head-on while still figuring out how to use it. Experts guardrails will not stop employees from using AI tools as they significantly enhance productivity and save time. Therefore, company CIOs must confront this issue and explore mitigation strategies that align with their organization's tolerance for risk. Inevitably, employees with good intentions will utilize these tools to increase their efficiency, so corporate tech leaders can prevent any potential harm to the organization by proactively addressing this trend and managing it effectively. believe Every employee's interaction with AI tools can be a potential point of vulnerability. The history of Shadow IT is marked by significant data breaches, such as the infamous incidents involving , which led to the public exposure of the personal data of 30,000 individuals. These historical precedents serve as a cautionary tale, emphasizing the need for rigorous data governance in the age of AI. unsecured Amazon S3 buckets Shadow AI is a more formidable challenge than Shadow IT for several reasons. First, the decentralized nature of AI tool usage means that the potential for data misuse or leakage is not limited to a technical subset of employees (e.g., developers) but extends across the entire organization. Additionally, inherently learn from the data they process, creating a dual layer of risk: the possibility of accessing sensitive data by AI vendors and the enhanced capability of bad actors to discover and exploit exposed data. AIaaS (AI as a Service) models Strategies to Tackle Shadow AI According to , regional VP of EMEA and Asia and head of digital transformation at SAS, tech leaders with a data-first mindset will be able to drive enhanced efficiencies in 2024 and beyond. This is because maximizing the benefits of generative AI tools is contingent upon well-organized data, necessitating robust data management practices encompassing data access, hygiene, and governance. Amir Sohrabi In his for CIO.com, Nick Brackney, Gen AI & Cloud Evangelist Leader at Dell Technologies, points to "three prescriptive ways" businesses should use to combat Shadow AI successfully. article First, establish a centralized strategy for generative AI usage, involving executive leadership to define use cases, create secure access, and protect data. This approach simplifies enforcement and scaling across the organization while requiring effort to build and identify easy wins to ensure success. Second, keep your data organized and understand which types should not be placed in public or hosted private cloud AI offerings, such as trade secrets and sensitive information. Use AI solutions that allow for complete control or do not retain conversation logs for these types of data. Third, control the AI service by bringing it to your data, whether on-premises or through secure cloud solutions, to leverage advantages in governance, employee productivity, and secure data access. This approach enhances the end-user experience, ensuring compliance and reducing the risk of data exposure. Crafting a clear is crucial for delineating improper AI practices that could potentially harm your organization and for guiding the integration of AI applications in line with data security protocols and risk management strategies. This policy serves as a benchmark, allowing decision-makers to evaluate the usage of AI tools within the organization against established guidelines, swiftly pinpoint any risk exposures, and determine the necessary corrective actions. AI Acceptable Use Policy Ethan Mollick, professor at the Wharton School of the University of Pennsylvania, another thought-provoking approach. He believes that traditional methods for integrating new technologies are ineffective for AI due to their centralized nature and slow pace, making it difficult for IT departments to develop competitive in-house AI models or for consultants to provide specific guidance. The real potential for AI application lies with the employees who are experts in their own jobs, suggesting that for organizations to truly benefit from AI, they must engage their workforce (aka "Secret Cyborgs") in using AI technologies. offers First and foremost, brands should acknowledge that employees at any level could possess valuable AI skills, irrespective of their formal role or past performance. Having detected the among their AI-savvy employees, companies must foster a collective learning environment such as crowd-sourced prompt libraries and create a culture that diminishes the apprehension around AI by offering assurances against job loss due to AI, promoting the use of AI for eliminating mundane tasks and encouraging more engaging work. secret cyborgs Establishing psychological safety is crucial to encouraging open AI usage among employees. Employers should be able to offer substantial rewards for identifying significant opportunities where AI can aid the organization. This could include financial incentives, promotions, or flexible working conditions and be handled through gamification. Today's organizations should act swiftly to determine how productivity gains from AI will be utilized, how work processes should be reorganized in light of AI capabilities, and how to manage potential risks associated with AI use, such as data hallucinations and IP concerns. This calls for a proactive approach to crafting comprehensive AI policies, engaging employees across all levels to leverage their insights, and fostering a culture that rewards AI-driven innovation. As technophiles and businesses navigate the complexities of AI integration, what strategies will you deploy to responsibly and innovatively leverage this transformative technology within your organization or personal projects, ensuring a balanced approach to privacy, security, and efficiency? Don’t forget to check out my previous article to discover ! AI’s dirty secret

Shadow AI: Reshaping the Future, But at What Cost?

শ্যাডো এআই: ভবিষ্যতকে পুনর্নির্মাণ করা, কিন্তু কী মূল্যে?

Schatten-KI: Die Zukunft neu gestalten, aber zu welchem Preis?

IA en la sombra: remodelando el futuro, pero ¿a qué precio?

Shadow AI : remodeler l’avenir, mais à quel prix ?

शैडो एआई: भविष्य को नया आकार देना, लेकिन किस कीमत पर?

シャドウ AI: 未来を再構築するが、その代償は?

Shadow AI: 미래를 재편하려면 비용이 얼마나 듭니까?

Shadow AI: remodelando o futuro, mas a que custo?

Теневой ИИ: меняя будущее, но какой ценой?

Shadow AI: Geleceği Yeniden Şekillendirmek, Ama Maliyeti Ne Olacak?

Shadow AI: Định hình lại tương lai, nhưng với cái giá nào?

影子人工智能：重塑未来，但代价是什么？

In the first half of 2023, there was a lot of excitement about investing in technology to achieve impactful enterprise-wide outcomes. However, the practical value of technology enablement doesn't just come from adopting innovations. It's about bringing continuous improvements to the core of company operations and business processes. In 2024, tech executives should approach new technologies with a deliberate and realistic mindset, ensuring each effort serves a purpose and any experiment is purpose-driven. In this article, I’ll analyze and explore how they will impact global businesses. the top 5 enterprise technology trends of 2024 Trend 1: Generative AI Despite polarized opinions and exaggerated hype surrounding generative AI, it will progress beyond offering easy access to complex knowledge and provide new capabilities, including the below. aids accurate budgeting and financial forecasting, empowering enterprises to plan revenue and expenses with greater certainty. Analyzing historical data, GenAI helps identify promising trends for potential profit opportunities. Enhanced asset management. Generative AI GenAI tools automate tedious data entry tasks, streamlining the processing and systemization of large documents within seconds. This enhancement to data processing has around of executives optimistic about boosting productivity across their organizations. Automated data entry. 72% Generative AI contributes to more effective fraud and suspicious activity detection, leading to increased customer satisfaction, improved accuracy, and cost reduction, as demonstrated by JPMorgan Chase. Intelligent fraud detection. Facilitating data management and information retrieval, GenAI enables employees to complete tasks and generate reports more rapidly. Customers benefit from fast and detailed responses to individual queries, bypassing static FAQ pages. Smart contextual search. Combining enterprise databases with generative AI opens new avenues for customer operations, offering hyper-personalization options. Employees can input relevant prompts for models to produce exclusive, specialized texts, visuals, or messaging tailored to specific partner needs. Personalized content generation. The effectiveness of GenAI technology hinges on data quality and the skills of engineers, so it’s important to assemble a strong team of AI strategists and data scientists. They should be capable of developing tailored solutions aligned with an organization's tech and security requirements. Trend 2: ML operations Also called MLOps or industrialized machine learning, machine learning operations refers to the processes and practices that speed up and scale the development of ML models within a company. This includes support, performance monitoring, and continuous improvement. MLOps functions within a system of software and hardware solutions, bringing enhanced value and functionality to enterprises. Industrialized ML helps businesses speed up analytics, identify production issues, and faster. transform PoC projects into products The adoption of industrialized machine learning has surged, and more companies are currently actively investing in MLOps. According to IDC, approximately of businesses are expected to implement industrialized ML by 2024. 60% We can clearly see the shift of ML technology from its pilot stage to a new phase of proactive and decisive adoption. Enterprises are now planning to achieve a competitive advantage through industrialized machine learning—and they are confident about achieving success. Trend 3: Intelligent enterprise apps Gartner predicts that by 2026, approximately of newly developed apps will incorporate AI for 30% better decision-making and personalization. The rise of intelligent apps is explained by the growing use of mobile payment options, wearable apps, and apps with VR and AR features. Modern apps better the UX by ensuring more accurate command execution and providing more detailed analytics, often incorporating AI algorithms and machine learning. So, why are intelligent applications considered valuable for the future of enterprise? AI-powered apps process large volumes of data from various sources and deliver real-time insights to users. Augmented analytics. By omitting irrelevant data, intelligent apps provide leaders with more informative reports, enabling them to make impactful decisions. Efficient decision-making. Such apps assist sales representatives in lead generation and nurturing by scoring leads based on behavior data analysis. Streamlined sales operations. Intelligent apps gather data on business processes and employee performance, identifying opportunities to reduce repetitiveness and enhance employee engagement. Management shortcut discovery. Intelligent software applications are also praised for their continuous improvement capabilities. With ML models and AI algorithms, these apps can adapt to new requirements, updating their output and maintaining high-quality performance. Trend 4: Web3 development Web3 technology finds applications across various sectors from banking, financial services and insurance (BFSI), media, and entertainment to logistics and retail because of the additional trust it provides. Key aspects of web3 adoption include: Web3 shifts power dynamics by giving businesses more control over their assets. The framework emphasizes transparency and equal responsibility among participants. Reduced authority intervention. Web3's focus on transparency opens doors to innovative business models and ensures fairness through automation, smart contracts, and a shared truth source among stakeholders. New business model development. Web3 integration into business requires regulatory frameworks. That’s why early adopters play a key role in normalizing and advocating web3 technology to set the groundwork for rules and standards. Web3 regulations. The increasing investment in decentralized technology stimulates the development of energy-efficient solutions, a crucial step for organizations that want to maximize their potential in industry 4.0 before transitioning to industry 5.0. Trend 5: Cloud computing As businesses shift to the cloud, its influence on current and future enterprise processes remains profound. Here are key innovative directions in which cloud computing is evolving: Integrating edge computing with cloud technology helps organizations reduce data transfer costs, achieve better latency, and reinforce cybersecurity. Combined cloud and edge computing. This strategy benefits developers, fostering workload distribution, cross-department collaboration, and flexibility in costs and investments. Hybrid models. AI-powered tools enhance cloud computing service and security monitoring, providing real-time insights crucial for decision-making and data analytics. This combination has led to the rise of Artificial Intelligence as a Service. AI/ML integration. Businesses will continue investing in expanding internal development teams with professionals having essential cloud computing skills to explore the potential of the latest cloud-powered directions. How do I choose the right direction? Picking the right enterprise technology from the tendencies of 2024 is simpler than it seems. Choosing the most popular trend might not be what defines the future of an enterprise. It's about finding technology that fits the specific needs of a business. Don't hyperfocus on trends. Not all trends stay relevant. Focus on innovations that maintain strong positions over time and offer continuous improvement for long-term value and ROI. Consider the results expected at least five years after implementing technology. Keep reusability in mind. Top enterprise technology trends may seem promising, but the adoption journey is long and complex. Connect wishes to reality by calculating estimated ROI and evaluating potential organizational resistance. Stay realistic. Also published . here

2024 Tech Landscape: 5 Trends Driving Operational Change

Continuous Integration and Continuous Deployment (CI/CD) have become necessary practices in this world where technology is moving fast, but security remains a top concern. CI/CD pipelines are the core of software delivery, but they can also show great weakness when it comes to security. With how complex software development is growing, it is important to have measures that will ensure the safety of . This is where AIOps comes into play, as the secret weapon for CI/CD security. CI/CD pipelines The State of CI/CD Security Understanding the current situation is crucial. CI/CD pipelines face a fair share of security issues due to their continuous nature, involving frequent code changes, builds, tests, and deployments. Here are some common challenges: 1. : Nasty code can sneak into the pipeline and spread through deployments, opening the door to security problems. Code Injection 2. : Sometimes, sensitive data slips out during the CI/CD process, putting user privacy and compliance at risk. Data Leakage 3. : If permissions aren't set up right, it's an open invitation for unauthorized access and potential breaches. Access Control 4. : CI/CD leans on third-party bits, which can carry vulnerabilities that threaten the whole process. Dependency Vulnerabilities With these challenges in mind, taking proactive security steps is a must, and AIOps steps in as the solution. Introduction to AIOps in CI/CD Security AIOps, short for Artificial Intelligence for IT Operations, is an advanced approach that uses artificial intelligence and machine learning to improve various aspects of IT management. In CI/CD security, AIOps plays an important role by offering real-time monitoring, data analysis, predictive analytics, and automated responses to security threats. Here's how AIOps can strengthen security in your CI/CD pipelines. Automated Threat Detection A key aspect of AIOps is its capacity to automatically find security issues in CI/CD pipelines. By constantly monitoring pipeline actions, it can identify deviations from the norm. It checks code changes, the building process, and deployment phases for signs of wrongdoing. If someone attempts to slip in unauthorized code, AIOps promptly detects it and raises a security alert. Real-time Monitoring and Response Security in CI/CD needs constant watchfulness. AIOps is great at this, noticing odd things right when they happen. In a CI/CD setup, this means acting fast in security situations. For instance, if there's an unusual data transfer during deployment, AIOps can trigger alerts and take steps to stop it, preventing possible breaches. Predictive Security Analysis AIOps doesn't only respond at the moment; it's also proactive. Thanks to predictive analytics, it can foresee security problems before they happen. By studying past data and spotting patterns, it can predict possible threats and weak points in the pipeline. This helps security teams take steps ahead of time to keep things safe during the whole CI/CD process. Case Studies: Real-world Success with AIOps in CI/CD Security Now that we've covered the principles of AIOps in CI/CD security, it's essential to see how these principles are applied successfully in the real world. Several organizations have harnessed the power of AIOps to enhance their security in CI/CD pipelines. Here are a few notable companies: Google Amazon Microsoft Netflix Spotify Airbnb Uber Walmart Target Capital One JPMorgan Chase Bank of America These companies are tapping into the power of AIOps to automate security chores, speed up threat detection and fixes, and strengthen the security of their CI/CD pipelines. Take Google, for instance; they're using AIOps to sniff out and tackle security issues in their Kubernetes clusters. Amazon is also on board, using AIOps to automate security checks in their CI/CD pipelines. And Microsoft? They're making use of AIOps to dig into security logs and spot any fishy stuff going on. Implementation Strategies for AIOps in CI/CD Security When it comes to bringing AIOps into CI/CD security, it's a smart move that needs careful planning. Here are some important strategies to keep in mind: 1. : You've got to pick the AIOps tools that fit well with your CI/CD pipeline and security needs. Tool Selection 2. : Make sure that AIOps smoothly blends with your existing CI/CD tools and processes. Integration 3. : Figure out the right levels of monitoring and how you'll respond to security issues tailored to your specific pipeline. Monitoring Thresholds 4. : Train your security and operations teams so they can use AIOps tools effectively and act on responses. Staff Training 5. : Be sure to deal with concerns about data privacy and follow the rules in your industry. AIOps should always operate within legal and regulatory boundaries. Data Privacy and Compliance Benefits of AIOps in CI/CD Security Adopting AIOps into CI/CD security offers several advantages that can significantly impact your software development process: 1. : AIOps makes detecting threats almost instant, speeding up the process of spotting and dealing with potential security issues in your CI/CD pipeline. Swift Threat Detection 2. : Thanks to automation and real-time monitoring, AIOps allows for rapid responses to security incidents, keeping their impact to a minimum. Quick Response Times 3. : AIOps strengthens your overall CI/CD security with its automated threat detection and predictive analysis, lowering the chances of security breaches. Enhanced Overall Security 4. : AIOps can cut operational costs by automating many security tasks, freeing up your teams to focus on more strategic security efforts. Cost Savings 5. : AIOps systems can be set up to ensure compliance with industry standards and regulations, reducing the risk of non-compliance. Enhanced Compliance Challenges and Considerations While AIOps is a powerful ally in CI/CD security, there are some challenges and things to keep in mind: 1. AIOps deals with sensitive data, so safeguarding it and meeting privacy rules is crucial. Data Privacy: 2. : AIOps can sometimes trigger false alarms. It's important to tweak these systems to prevent your security team from being flooded with unnecessary alerts. False Alarms 3. : Setting up and managing AIOps systems requires a certain skill set. You might need training or skilled staff to handle it effectively. Skill Requirements 4. : Getting AIOps to work smoothly with your existing CI/CD tools can be tricky. Planning the integration carefully is a must. Integration Complexity Future Trends and Innovations The future of AIOps in CI/CD security looks bright, with some exciting trends and innovations on the horizon: - : AIOps will increasingly rely on understanding patterns and behavior to spot security threats. Behavior-Based Threat Detection - : AIOps will keep getting better at working smoothly with different CI/CD platforms, making it more adaptable. Cross-Platform Integration - : AIOps will become even smarter at predicting threats, making it more accurate. Enhanced Predictive Analytics , AIOps is like the secret weapon for CI/CD security. Its automated threat detection, real-time monitoring, and predictive analysis are key to keeping your CI/CD pipelines safe and sound. As software development keeps changing, AIOps will play a bigger role in securing the way your software gets delivered. In conclusion The future of CI/CD security depends on those who use AIOps to build secure, reliable, and efficient software delivery pipelines.

AIOps: The Secret Weapon for CI/CD Security

Dive into how AI is transforming the investment landscape Remember the early days of robo-advisors? They were like the first smartphones—revolutionary but limited. Now, generative AI is stepping in, offering more nuanced and personalized financial services. For instance, Q.ai offers customers investment kits picked by an AI that considers various data points, from market metrics to social media sentiment. 📊 Performance Metrics While AI's promise is high, its performance is still under scrutiny. ETF Managers Group's AIEQ, which uses IBM's Watson, has returned 4.9% over the last five years, trailing the S&P 500 index fund's 11.78%. It's like a new athlete who shows promise but has yet to win significant titles. 🛠️ Guardrails and Accountability AI isn't given free rein. Companies like Q.ai and AIEQ (see examples below) have human oversight to monitor AI output for biases and errors. Think of it as a self-driving car with a human driver ready to take over in complex situations. 🌐 Accessibility and Inclusion AI is democratizing financial advice. Firms like Fisdom in India use automated tools to offer financial guidance to a broader base of customers. It's like opening up an exclusive club to the general public, making high-quality advice accessible to all. 🤖 The Future: AI in the Back Office Even if AI isn't directly advising clients, it's finding a place in back-office systems. For example, it summarizes meeting transcripts, monitors risky transactions, and generates reports. Examples of startups / smaller companies using AI for investment management: 🤖 Q.ai Location: Jersey City, N.J. About: Q.ai offers no-fee "investment kits" consisting of 4 to 20 securities in a market sector. These kits are selected by an AI that evaluates market metrics, news, Google search trends, and social media sentiment. 📊 ETF Managers Group’s AIEQ Location: Summit, N.J. About: AIEQ uses IBM’s Watson AI platform to analyze millions of data points from various sources like news, social media, and financial statements of over 6,000 U.S. companies. 🌐 Nemo Location: Abu Dhabi About: Nemo uses a ChatGPT model to allow investors to ask questions they might typically ask a human broker. The company emphasizes that they don't let their AI go "off-leash." 📈 Toggle AI Location: Not specified About: Toggle AI aims to implement ChatGPT that will be programmatically constrained to provide reality-grounded answers to investors' questions. 🌟 Fisdom Location: India About: Fisdom uses automated tools to provide financial guidance to a broader base of customers, including individuals with low net-worth. 💼 Betterment Location: New York About: Betterment, one of the first mass-market robo-investing firms, remains cautious about using AI for financial advice. However, they do use AI in their back-office systems for tasks like summarizing meeting transcripts and diagnosing software issues. Examples of incumbent banks using AI for investment management: JPMorgan Chase: This financial giant is leveraging AI to optimize asset management and offer personalized investment solutions. Morgan Stanley is using AI to enhance its asset management strategies. UBS: Known for its wealth management services, UBS is adopting AI to improve its investment strategies and customer engagement. HSBC: This global bank is using AI to bolster compliance and risk management functions and augment and automate data analysis in the investment sector. 🔮 Summary The integration of generative AI into investment management is a significant development, offering more personalized and nuanced services. Performance metrics are crucial for assessing the efficacy of AI-driven investment platforms, and they currently show a mixed picture. The human oversight in AI-driven systems is a necessary safeguard, ensuring that the technology does not go "off-leash." Whether it's automating data analysis, enhancing customer engagement, or optimizing asset management, AI is playing a pivotal role in shaping the future of investment management. Excerpt from One More Thing in AI Newsletter. Get smarter on AI in 5 mins. Carefully curated from hundreds of sources for business leaders and founders. ) Join 1000+ subscribers for free.

How AI is Transforming the Investment Landscape

By Aoibhinn Mc Bride Holding your hands up and taking accountability for a mistake is never easy. So, spare a thought for the who are now admitting that hastily issuing return to office (RTO) orders wasn’t their best decision. 80% of business leaders A found that most leaders made their decision around forcing staff back to the office by analyzing keycard monitoring data and by seeing how many times meeting spaces were utilized, rather than by speaking to employees and taking their thoughts around returning to the office five days per week into consideration. new report by Envoy Additional data compiled by Owl Labs for its found that 69% of employees believe companies are insisting on a return to office because of traditional work expectations rather than functional benefits. State of Hybrid Work 2023 report Using employee monitoring data to ensure their staff are coming into the office is nothing new—companies including and have been using employee badge swipes to monitor in-office attendance for over a year—but is counting swipes the best way to make decisions around workplace location? JPMorgan Chase Google While 94% of those who took part in the Owl Labs survey shared that they could be convinced to come back to the office, 56% said their level of work-related stress increased since last year, highlighting that being in the office full-time isn’t serving everyone. Moving Forward So what can you do if your employer is insisting you wave goodbye to your home office for good and return to the office permanently? For starters, if commuting to an office every day is going to leave you worse off financially, you could ask for a pay rise to cover the cost—29% of those Owl Labs spoke to expect a pay increase if forced back to the office. Or perhaps you can speak to your manager about revising its technology so that it facilitates remote work more effectively, meaning you can adopt a hybrid working model instead. Additionally, you could try and negotiate a four-day week where you consolidate your work into four days but your . productivity and output stay the same However, if your employer is refusing to meet you half or even part of the way, one solution is to find a new job in a company that facilitates remote work. If so, the is the perfect place to start your search. HackerNoon Job Board It features thousands of jobs in companies that are actively hiring remote workers, including the three below. Senior Sales Engineer, Coder, Remote Coder is on a mission to build enterprise software that makes software developers’ lives easier and keeps them in flow. We are looking for a who will work with enterprise account executives to qualify, progress, and close revenue opportunities with Fortune 500 customers for Coder’s self-hosted Development Environment Platform. Senior Sales Engineer In this role, you will be a stakeholder in the technical relationship with customers, demonstrate the Coder platform from both the end user (developer) and administrator points of view, and install the Coder platform on various cloud and on-premise Kubernetes and VM infrastructure. . Find out more information here Product Manager, Search Platform Integration, Virtuoso Ltd, Remote Virtuoso is the leading global travel agency network specializing in luxury and experiential travel. The will oversee the integration of the Algolia search platform and play a crucial role in enhancing the search capabilities of the platform, ensuring a seamless and intuitive search experience for travel products, travel advisors, network-specific content, and travel inspiration content for valued travelers. Product Manager, Search Platform Integration As such, you will work closely with technical teams to define data structures, indexing strategies, and search algorithms that optimize search results across various content types. . See the full job description here Salesforce Solutions Architect, Vantage Point Consulting, Remote As a , you will play an integral role in the high satisfaction of Vantage Point's clients through the design and development of scalable, high-quality Salesforce services and solutions. Salesforce Solutions Architect The Solution Architect is responsible for owning the overall design of the technical application and recommending best practice solutions based on client business needs. . View additional details here Find your next job in tech today via the HackerNoon Job Board

Does Your Boss Regret Asking You to Come Back to the Office?

আপনার বস কি আপনাকে অফিসে ফিরে আসতে বলার জন্য অনুতপ্ত?

Bereut Ihr Chef es, Sie gebeten zu haben, ins Büro zurückzukehren?

¿Se arrepiente su jefe de haberle pedido que volviera a la oficina?

Votre patron regrette-t-il de vous avoir demandé de revenir au bureau ?

क्या आपके बॉस को आपको कार्यालय वापस आने के लिए कहने पर पछतावा है?

あなたの上司はあなたにオフィスに戻ってくるよう頼んだことを後悔していますか?

당신의 상사가 당신에게 사무실로 돌아오라고 한 것을 후회합니까?

Seu chefe se arrepende de ter pedido para você voltar ao escritório?

Сожалеет ли ваш начальник, что попросил вас вернуться в офис?

Patronunuz Ofise Geri Dönmenizi İstediğinden Pişman mı Oldu?

Sếp của bạn có hối hận khi mời bạn quay lại văn phòng không?

你的老板后悔让你回到办公室吗？

In the ever-changing modern business landscape, business growth strategies, once grounded in industry experience, competitive benchmarking, and a fair share of professional intuition, are being reshaped by the influence of . The sheer volume, variety, and velocity of data available to businesses today offer invaluable insights promising rapid growth and unprecedented efficiency. Big Data At the forefront of this revolution marches machine learning. Not just a passing trend, machine learning provides a rigorous mathematical framework to interpret patterns and make strategic decisions based on accurate predictions. But how exactly does machine learning modify and transform business growth strategies? And in what ways can its advanced algorithms help a company aiming for market leadership? In this article, I will try to answer these and other questions, exploring the connection between machine learning and efficient business development. Content Overview How Machine Learning Affects Modern Business What Are the Basic Algorithms of Machine Learning How Machine Learning is Applied to Business Growth in Practice A Step-by-step Guideline to Implementing Machine Learning for Business Growth Technology Limitations and Practical Considerations Conclusion How Machine Learning Affects Modern Business In today's business environment, data plays a central role. Every customer interaction and transaction generates data that offers insights into market dynamics. The core advantage of machine learning in business is its ability to transform this extensive data into actionable decisions. is a computational method that allows its practitioners to identify patterns and make data-driven decisions without explicit programming. In business contexts, it enhances decision-making by eliminating human biases. Instead of relying on lengthy manual analysis or basic market surveys, machine learning provides an in-depth, objective view by examining numerous variables simultaneously. Machine learning Take product pricing as an example. Instead of using conventional methods like cost-plus pricing, machine learning offers dynamic pricing models. These models factor in real-time demand, inventory, competitor prices, and even external events to recommend optional pricing. Similarly, in customer relations, machine learning refines the broad understanding of target audiences. By evaluating browsing patterns, purchase histories, and customer feedback, it assists in building personalized marketing strategies, boosting conversion rates and customer loyalty. This series of examples can be continued . ad infinitum The benefits machine learning brings to present-day business are innumerable. Today, in the third decade of the 21st century, the question is not whether companies should adopt machine learning, but rather how swiftly they can integrate it into their internal processes to avoid falling behind the competition. But first, let us ask ourselves a question, what do people mean by machine learning? What Are the Basic Algorithms of Machine Learning Machine learning leverages an intricate combination of mathematics and computation, yet it is much more than just a set of convenient tools for processing large volumes of data. Machine learning is a discipline rooted in established mathematical principles, which ensures the accuracy and reliability of the results it delivers. To give you a peek into the nature of this technology, I will provide a brief description of some basic machine learning algorithms. While this section would tell nothing new to experienced professionals utilizing the power of machine learning in their jobs, like data analysts, I hope that a reader new to the topic will find it informative. a way to predict one value based on another by discerning patterns from available data. Essentially, it determines the relationships between different variables, influencing one another. Linear regression, for instance, assumes a straight-line relationship between two variables, while polynomial regression describes curved relationships; yet another example is logistic regression which predicts the probability of a binary outcome (true or false). By understanding these relationships, businesses can predict the effect of a change in one variable on another. For example, Airbnb uses regression analysis to suggest optimal pricing for hosts based on factors such as location, property type, time of year, and other listings’ prices. Similarly, Zillow's "Zestimate" applies linear regression to predict house prices based on features like size, location, and number of rooms, leveraging vast amounts of past sales data. Regression Analysis provides play a pivotal role when dealing with massive volumes of unlabeled data. They segment data into distinct groups, with items put in groups based on inherent similarities – and differences from other groups. K-means, for example, partitions data into 'k' number of centroids, and all data points are clustered around these centroids based on similarity. The New York Times, as an example, utilizes clustering to group news articles for readers, ensuring that similar stories, whether thematic or topical, can be navigated with ease. Clustering algorithms involve analyzing available data and learning patterns to apply to new, unknown data. Often used in conjunction with clustering, classification is about assigning predefined labels to data points. One well-known application is in the finance sector. Banks like JPMorgan Chase & Co. leverage classification to discern between 'fraudulent' and 'legitimate' client transactions, drawing upon patterns in historical data. In the healthcare domain, IBM's Watson Health utilizes classification algorithms to assess medical imaging, helping doctors determine whether a tumor is benign or malignant based on specific attributes. Classification methods l: extracting meaningful, actionable patterns from data. By laying out a mathematical framework, machine learning ensures these patterns are not coincidences but have statistically significant foundations, providing reliable insights that businesses can use. This leads us to the next part of this article – a deep dive into the real-life applications of machine learning in business development. All these algorithms share a common goa How Machine Learning is Applied to Business Growth in Practice Machine learning’s theoretical foundation is captivating, but where its transformative power shines is in its ability to spark business growth through practical applications. To better understand its impact, let us dive into how it has reshaped various sectors in recent years. Nowadays, machine learning plays a key role in shaping . By analyzing vast data sets ranging from local consumer behavior, cultural preferences, and competitive landscapes to regulatory requirements, machine learning models predict the success rate of a product or service in a particular region or demographic. Such data-driven insights can guide businesses on where to open new stores, how to adjust product features for local tastes, or even when to launch for optimal impact. This predictive capability empowers businesses to make informed, strategic decisions, optimizing investments and amplifying their chances for successful market expansion. Market Expansion Strategies In the realm of , gone are the days of relying solely on traditional demographic-based methods. Clustering algorithms have revolutionized this space, enabling businesses to segment their clientele based on buying behavior, browsing patterns, and even sentiments on social media. This radical shift has paved the way for marketing campaigns that more precisely resonate with individual consumers, ultimately fostering stronger customer acquisition and retention rates. Customer Segmentation The complexity of – predicting future sales trends – has been an age-old challenge. But with machine learning, tools like regression and time series forecasting have introduced a new layer of precision. By intricately analyzing both historical data and external variables such as economic indicators or seasonal patterns, businesses now possess sophisticated tools to project revenue, enhancing their strategic decision-making process. Sales Forecasting In the dynamic world of commerce, setting the right price can make or break a product's success. Machine learning aids businesses in devising smart strategies by analyzing a combination of internal data (like production costs and desired margins) and external factors (like market demand, competitor pricing, and seasonal influences). These algorithms can dynamically adjust prices in real-time, promptly responding to changes in the market environment. By ensuring that products are priced competitively and profitably, businesses can boost sales volumes, maximize revenues, and gain a strategic edge in crowded markets. Pricing Optimization One more vital area where machine learning has showcased its value is in . By analyzing historical workflow data, machine learning can identify bottlenecks and inefficiencies holding back the business growth, or repetitive tasks ripe for automation. For instance, in manufacturing, predictive maintenance powered by machine learning can forecast when a piece of machinery is likely to fail, allowing for proactive maintenance, reducing downtime, and enhancing overall production efficiency. Operational Efficiency And last but not least, has also witnessed a profound transformation. Instead of grappling with the perennial challenges of overstocking and stockouts, businesses now harness machine learning's predictive analytics to predict future demand. This means more balanced stock levels, optimized supply chain operations, and the mitigation of lost sales opportunities. Inventory Management While these examples highlight just a few of the many ways machine learning has optimized business operations and business development, it is paramount to remember that the effectiveness of these models relies on data quality and the right algorithmic choices. A careful and informed approach to integration is necessary when deploying machine learning techniques and algorithms to develop new and facilitate existing business growth strategies. With my extensive experience in this area – harnessing the power of machine learning to benefit businesses, I can provide some practical insights on the matter. A Step-by-step Guideline to Implementing Machine Learning for Business Growth Integrating machine learning into business processes extends beyond merely deploying algorithms. It demands structured planning, teamwork, and continuous adjustment. Here are the key steps businesses should consider when embracing the power machine learning provides in their business development strategies: Before implementing data analysis or choosing a model, a company has to specify the intended objectives. Whether the aim is to expand to new markets, increase customer lifetime value, or optimize ongoing operations for better profitability, setting precise, quantifiable growth targets ensures machine learning projects stay on track with the set business goals. Establish clear business growth objectives Incorporating machine learning for growth is more than a data scientist's task. Effective integration often calls for insights from market strategists, sales experts, customer relationship managers, and tech specialists. By fostering a collaborative environment, businesses can ensure the resulting models are both technically robust and tailored to drive business expansion. Form a cross-functional team with a focus on growth Machine learning offers a variety of algorithms and tools, applicable to different growth challenges. For example, clustering algorithms can identify new market segments, while time series forecasting might predict sales trends. The choice of tools is dictated by the specific growth strategies. Select appropriate tools and algorithms After a model is created, the next step is to assess its performance with real-world data. Initial outcomes might not always hit the mark, but machine learning thrives on adjustment. Feedback and results guide the necessary model revisions for enhanced precision, and it is crucial to measure outcomes based on business growth indicators in focus – whether it is market share expansion, customer acquisition rates, or an increase in revenue. Adhere to iterative testing and refinement keeping business metrics in mind With a refined model ready, you can move to the deployment stage, integrating the model into strategic execution points. This can entail embedding insights into sales dashboards, incorporating predictions into marketing campaigns, or using forecasts in supply chain adjustments. It is critical to ensure smooth integration with current business infrastructure, enabling actionable insights that contribute to growth. Deploy and integrate As market conditions, business strategies, and customer preferences keep evolving, machine learning models must adapt. Regular monitoring is essential to track performance, with updates accommodating new data or changing market dynamics. Maintain regular oversight and adjustments By following a structured approach and implementing machine learning with clear growth-centric goals, businesses can unlock untapped growth and innovation opportunities. Still, even machine learning has its limitations, and the incorporation of its techniques and methods into business operations presents certain challenges to take into account before you start. Technology Limitations and Practical Considerations As businesses rush to harness the capabilities of machine learning, it is crucial to approach its adoption with a balanced perspective. Although the concerns I address here are common when implementing machine learning algorithms in general, if anything, they hold even more true when incorporating machine learning with the explicit goal of driving business growth. Understanding these limitations is of momentous importance to realizing the full potential of machine learning in bolstering growth strategies. One of the main things to consider while employing machine learning in your business is . Machine learning models thrive on data, and if the input data is flawed or incomplete, the output will mirror these imperfections. Businesses have to invest time and resources in data cleansing, preprocessing, and validation to ensure the reliability of the insights derived. data integrity Another critical thing one should watch out for in data sets is . Machine learning bias means that an algorithm will produce flawed results due to erroneous (biased) assumptions in the data the algorithm is trained on. If historical data carries inherent biases, the algorithm will likely reflect them. For businesses, this can lead to skewed customer insights or even discriminatory practices, and so active measures must be taken to identify and rectify such biases in the data beforehand. potential biases In the pursuit of high accuracy, there is a temptation to build complex models that perform exceptionally well on training data but falter when exposed to new, unseen data. This phenomenon, known as , can lead businesses astray with overly optimistic predictions or recommendations. Regular validation and testing against independent datasets are vital to ensure the model's generalization capabilities. overfitting Some advanced machine learning models, especially deep neural networks, are often termed "black boxes" due to their complex architectures. While they might offer superior accuracy, their workings can pose severe . For businesses, this poses a challenge in understanding and explaining the rationale behind specific predictions or decisions, especially in regulated sectors where transparency is imperative. interpretability issues In essence, while machine learning is a formidable tool in the business arsenal, its implementation demands a judicious blend of technical expertise and domain knowledge. The promise of machine learning is not in its infallibility but in its capacity to elevate decision-making when applied with due consideration of its limitations. Conclusion As the dynamics of the business world continue to evolve, the importance of staying adaptive and forward-thinking is paramount. It is evident that every day, data plays an increasingly important role in shaping modern commerce. As I hope I was able to demonstrate in this article, decision-making in business, and especially in building balanced and effective business growth strategies, has been deeply and irreversibly transformed by the integration of machine learning, adding depth and precision to once purely intuition-driven processes. Today, embracing machine learning is more than just a strategic move – it is a necessity for businesses aiming for consistent growth in a digital age. Yet, naturally, integrating this advanced technology is not without its complexities. Constructing systems that not only gather but deeply analyze data to provide actionable insights is a task that requires both innovative thinking and unyielding persistence. And while machine learning offers businesses innumerable advantages, it is the combination of this technology with human expertise that creates a robust foundation for productive business strategies. This blend ensures that decisions are both data-informed and grounded in human judgment – something that no technology has been able to replace.  At least – for now.

JPMORGAN CHASE

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JPMORGAN CHASE

jpmorganchase.com

10K+ employees

Since 2000

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EVERGREEN INDEX #5585

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