On CRM: Do You Really Need A CRM Consultant? Really?

 Do you need an outside consultant to help you implement a CRM system for your business? The answer is: it depends.

How Corporate Leaders Can Help Software Developers Reach Their Full Potential

There’s an image many have of software developers—they sit in a dark room and code endlessly. But unfortunately, more often than not, developers don’t have the leeway to code in an efficient, uninterrupted manner.

How Machine Learning Will Transform Your Industry

 Machine learning is a rapidly growing field with endless potential applications. In the next few years, we will see machine learning transform many industries, including manufacturing, retail and healthcare.

In manufacturing, machine learning can be used for quality control, automation and customization. For example, machine learning can be used to detect defects in products before they reach consumers. It can also be used to automate repetitive tasks such as assembly line work. And finally, manufacturers will increasingly use machine learning to customize products for individual consumers.

In retail, machine learning can be used for data analysis to help businesses make better decisions about inventory and pricing. Personalization will become more common, with retailers using machine learning to recommend products to customers based on their past behavior. Robotics will also become more prevalent, with machine learning being used to automate tasks such as shelf stocking and order picking.

In healthcare, machine learning can be used for diagnostics, treatment and prevention. For example, machine learning can be used to diagnose diseases earlier and more accurately. It can also be used to develop personalized treatments based on a patient's characteristics. Machine learning can also be used for preventative care, such as identifying risk factors for disease and providing tailored recommendations for healthy living.

So far we have only scratched the surface of what is possible with machine learning. As technology continues to evolve, we will see even more amazing applications of this transformative technology.

Machine Learning In Manufacturing

In the past, quality control for manufactured goods was a time-consuming and expensive process that required human inspectors to examine each item for defects. However, machine learning can be used to automate this process by training algorithms to identify defects from images or other data sources. This can help reduce the cost of quality control while also increasing the accuracy of the inspection process.

Automation

Machine learning can also be used to automate manufacturing processes. For example, robots that are equipped with machine learning algorithms can be trained to perform tasks such as welding or fabricating parts. This can lead to a more efficient manufacturing process and can free up human workers for other tasks.

Customization

Another way that machine learning is transforming manufacturing is by enabling customization at scale. In the past, it was difficult and expensive to create customized products due to the need for manual labor and individualized production lines. However, machine learning algorithms can now be used to automatically generate custom designs based on customer specifications. This allows manufacturers to quickly and easily produce personalized products without incurring significant additional costs.

Machine Learning In Retail

In the past, retailers have relied on data from customer surveys and transactions to make decisions about their business. However, this data is often incomplete and doesn't provide a full picture of customer behavior. Machine learning can help solve this problem by analyzing large data sets to identify patterns and trends. This information can be used to improve customer service, optimize stock levels and make other strategic decisions.

Personalization

Machine learning can also be used to personalize the shopping experience for customers. For example, Amazon uses machine learning to recommend products that customers may be interested in based on their previous purchase history. This helps shoppers find what they're looking for more quickly and makes the overall shopping experience more enjoyable.

Robotics

Robots are increasingly used in retail settings to perform shelf stocking and order fulfillment tasks. While these machines cannot replace human workers completely, they can free up employees' time to focus on more critical tasks, such as helping customers. In the future, robots may become even more involved in the retail sector as machine learning technology develops.

Machine Learning In Healthcare

Machine learning is already being used in healthcare to diagnose diseases. For example, Google has developed an algorithm that can detect breast cancer based on images. In the future, machine learning will be used to diagnose more complex conditions such as Alzheimer's disease and cancer.

Treatment

Machine learning can also be used to develop new treatments for diseases. For example, a company called Insilico Medicine is using machine learning to develop new drugs for cancer and other diseases. In the future, machine learning will be used to develop more effective and personalized treatments for patients.

Prevention

In addition to diagnosing and treating diseases, machine learning can also be used to prevent them. For example, IBM's Watson system is being used to predict patients' risk of developing certain diseases. In the future, machine learning will be used to create more personalized and effective prevention plans for individual patients.

Conclusion

Machine learning is set to transform a wide range of industries in the coming years. In retail, machine learning will enable more accurate data analysis, personalization of products and services and even the use of robotics in stores. In healthcare, machine learning will revolutionize diagnostics, treatment and prevention. And in manufacturing, machine learning will improve quality control, automate processes and allow for greater customization. These are just a few examples of how machine learning will change the landscape of the industry as we know it. So whatever sector you're in, it's time to start preparing for the machine learning revolution.

While ML and associated technologies like natural language processing are gaining traction in current workflows, it's important to pay close attention to ethical standards that differentiate humans from machines. Today, ML has come to a point where it can replace humans in many intelligent tasks. The future is clearly AI/ML-driven, and it will eventually become part of our lives to the degree the mobile phone is. We will take it for granted. Given all of this, those using and developing AI must keep ethics in mind when dealing with it, whether that's focusing on consumer privacy rights or keeping up to date with laws and regulations surrounding the technology in this space.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource: https://www.forbes.com/sites/forbestechcouncil/2023/02/27/how-machine-learning-will-transform-your-industry/

ChatGPT, Machine Learning And Generative AI In Healthcare

Machine learning has finally captured the popular imagination in ChatGPT.

The free chatbot program, capable of generating a wide array of impressively human-like text from simple prompts, has chalked up copious headlines (and huge investment) since its public release late last year. In the space of eight weeks, Reuters reports, it attracted around 100 million active monthly users, making it “the fastest-growing consumer application in history.”

It is also already causing enormous debate about the future of journalism, academic testing, digital marketing and computer programming, among other specialties. It has even been dubbed the latest potential “Google killer.”

Without wading into any of those contentious issues, its potential for assisting in healthcare is incredibly exciting.

The Technology

First, let’s clarify the technology. The chatbot was developed by artificial general intelligence (AGI) research firm OpenAI on the company’s GPT-3 family of large language models (LLMs). It’s an example of conversational “generative AI”—basically machine-learning algorithms trained on massive troves of internet data to quickly generate new content (in this case, text) with minimal input. It can actually produce something from what it has “learned.”

While ChatGPT’s output is by no means perfect, the hype around it is warranted. The program and its underpinning models represent a big leap forward in sophistication and capability in natural language processing (NLP) technology—as well as an incredibly speedy evolution.

Consider that the final model of OpenAI’s GPT-2 deep learning neural network was released in November 2019 and was trained with 1.5 billion machine-learning parameters. The beta version of GPT-3 debuted in mid-2020 and was trained with more than 175 billion machine-learning parameters. By September 2020, Microsoft had licensed it for use in products. And by 2021, GPT-3 was fueling new Microsoft application features. This year, Microsoft has employed GPT-3 to add “intelligent recap” features to its premium Teams application, including “automatically generated meeting notes, recommended tasks, and personalized highlights,” and has just announced that the latest version of its Bing search engine will incorporate ChatGPT-like features.

OpenAI and Microsoft are not alone in advancing this type of technology. Alphabet (Google’s parent company) has its own experimental AI projects, such as Language Model for Dialogue Applications (LaMDA), and is currently releasing a ChatGPT-like conversational AI tool, dubbed Bard, to select testers. Meta (Facebook’s parent) and Quora have also joined the generative AI fray with their own chatbot examples. This technology seems to be everywhere all at once.

And that has a lot of people rethinking what generative AI makes possible, how quickly it can happen, and where it will make the biggest impact—healthcare is no exception.

Healthcare Potential

I have written before about the need for AI-powered decision intelligence and support systems in healthcare. Clinicians and healthcare workers could really use some relief from information and administrative overload. Generative AI may be able to help.

For example, last year Microsoft Research scientists published a paper on a project called BioGPT, “a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature.” They essentially took OpenAI’s GPT-2 and refined it with a large corpus of reputable biomedical literature, making a BioGPT that is better equipped to mine, analyze and “discuss” biomedical text—and that outperforms previous models on most tasks.

Now consider how such a domain-specific AI model might help with something like sepsis.

Mayo Clinic defines sepsis as “a potentially life-threatening condition that occurs when the body’s response to an infection damages its own tissues.” Sepsis can be caused by parasites, bacteria, fungal infections, and viruses, and physicians describe it as “hard to spot and easy to treat in its earliest stages, but harder to treat by the time it becomes evident.” Sepsis can present with a confusing “constellation of symptoms.” It is also the number one cause of death in hospitals.

Something like BioGPT could be used to:

• Analyze vast amounts of biomedical literature and extract relevant information related to sepsis to identify patterns and insights.

• Generate new biomedical literature related to sepsis, including combinations of hypotheses and theories that could guide future research.

• Aid in diagnostics and treatment for sepsis, and help in more quickly identifying targets for intervention.

In regard to a completely different but no less vexing area of healthcare, something like BioGPT could potentially disrupt the current healthcare coding and billing system. Costs associated with coding and billing in the U.S. are very high and “significantly exceed those in similar countries.” Generative AI could help by:

• Efficiently automating the process of coding medical procedures and services, freeing up time and resources for other critical tasks.

• Identifying and correcting coding errors, which are a common occurrence in the current system, reducing the risk of denied claims and other financial penalties.

• Identifying new and emerging trends in medical procedures and services to inform the development of new CPT codes, allowing healthcare providers to reflect the changing landscape of medical procedures and services more accurately.

The use of generative AI has the potential to greatly impact clinical understanding, diagnosis and treatment of complex medical conditions as well as improve the accuracy, efficiency and effectiveness of healthcare system function. This technology could quickly lead to improved patient outcomes, more streamlined processes, and reduced costs for both healthcare providers and patients.

We aren’t there yet, but it isn’t too early to start imagining ways for this technology to help.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource: https://www.forbes.com/sites/forbestechcouncil/2023/03/07/chatgpt-machine-learning-and-generative-ai-in-healthcare/?sh=49177ff1556f

Sprint Planning Tips And Tricks For Software Development Leaders

 Sprint planning is a critical step in the agile software development process, as it sets the direction and goals for the upcoming sprint. As a software development leader, it is essential to understand the importance of sprint planning and how to plan and execute sprints to achieve success effectively.

Why Sprint Planning Is Efficient

Sprint planning can help the development team stay focused on the project's goals, identify any potential roadblocks or challenges that may arise during the sprint, and develop a plan to address them.

We implemented sprint planning strategies so that we could manage our workflows more efficiently and improve the overall performance of the teams. We found that it also promotes better communication and collaboration among our team members, encouraging everyone to share their ideas, concerns and suggestions. This can lead to a better understanding of the project and a more cohesive team effort.

How Sprint Planning Aligns With The Agile Methodology

Agile methodology values adaptability and encourages teams to respond to change and be open to new ideas. Because feedback is an important part of our sprints, we've found that sprint planning can promote adaptability and determine how receptive our teams are to it. In our sprint review meeting, our teams share feedback, and we find ways to adapt to it.

Prioritizing Customer Needs

Agile methodology prioritizes customer needs and values the input of stakeholders. Sprint planning can help teams review the product backlog (a prioritized list of items that need to be completed for the project) and identify the highest priority items that need to be completed during the upcoming sprint.

Collaboration And Communication

Agile methodology values collaboration and communication among team members and encourages team members to share their ideas, concerns and suggestions—promoting better communication and cooperation among team members.

For us, it doesn't matter how many ceremonies we have; we always try to validate assumptions and communicate as much as possible within the teams. Our daily standups are our way to never make assumptions and to communicate as efficiently as possible.

Continuous Improvement

Agile methodology values continuous improvement. At the end of each sprint, we meet for the sprint review to give feedback and share our successes. This allows us to promote a culture of continuous improvement.

Self-Organization

The agile methodology encourages self-organization, where team members have the autonomy to make decisions and take ownership of their work.

Best Practices For Engineering Leaders Regarding Sprint Planning

Define The Sprint Goal

The sprint goal is a clear and concise statement that defines the overall objective of the sprint. It should be specific, measurable and achievable. For example, one of our sprint goals was to speed up the loading time of the app by 50%. To ensure the goal is well understood, we keep the communication going in our teams and use an issue-tracking system to manage our sprints in a flexible platform.

Review The Product Backlog

The product backlog is a prioritized list of items that must be completed for the project. The team should review the backlog and identify the highest-priority items that must be completed during the upcoming sprint. When reviewing our product backlog, we try to determine the most important tasks that add the most value to the customers.

Determine Sprint Capacity

Sprint capacity is the amount of work the team can realistically accomplish during the upcoming sprint. We determine our sprint capacity by analyzing performance over time and finding a way that's the most efficient for our teams. For example, we have alerts set up in case our teams feel the need to work on weekends, allowing us to know if the workload is too heavy.

Review And Adjust The Process

Reviewing the sprint planning process regularly and adjusting as needed can help ensure that the process is efficient and effective and that the team is progressing toward achieving its goals. At the end of each sprint, we check how our team performed in the sprint. The DORA metrics are a good performance indicator: How does the change failure rate look? How about our mean time to recovery? Based on factual data and numbers, we know where to focus and what to improve for the next sprint.

Provide Guidance And Mentorship

Engineering leaders should provide guidance and mentorship to the team members and help them identify and overcome any obstacles they might face during the sprint. Bottlenecks are something usual, and every team encounters them. We try to spot these obstacles and help our teams overcome them quickly. We've found that this can improve velocity as well as the overall developer experience.

Set A Definition Of "Done"

A definition of "done" is a clear and agreed-upon set of criteria to be met for an item to be considered complete. We define "done" by ensuring we have a shared notion of completeness and quality within the team. We have a list of criteria that establishes what "done" means for each user story.

Conclusion

Sprint planning is a crucial step in the software development process, and it is essential for software development leaders to plan and execute sprints to achieve success effectively. By going through the steps mentioned earlier, software development leaders can ensure that their team stays focused on the essential tasks and is working on items that will have the greatest impact on the project.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource: https://www.forbes.com/sites/forbestechcouncil/2023/03/01/sprint-planning-tips-and-tricks-for-software-development-leaders/?sh=55b3ae073349

What startup founders need to know about software development

 Agility is a startup’s competitive edge against mature businesses. Startups are generally more responsive to emerging customer demands. They can react faster than established businesses because those established businesses usually have longer decision chains.

However, agility requires intelligent allocation of available resources. Startups pivot many times along the way to a market-fit product. They must be prepared for rapid and cost-effective changes.

Startups should think about how they apply specific practices necessary to create a flexible growth strategy, accurately estimate the time and resources needed, keep effective processes up and running, and maintain enough room for necessary pivots. Let’s keep Otherwise, it is not allowed to create a marketable product. Some decisions (eg, related to product structure) can reduce agility. These decisions are not conducive to the latter view.

In order to move forward effectively, first time startups should keep the following details in their mind.

1. Beware of fixed price terms

A fixed price agreement provides the startup with a sense of cost control. Startups can know in advance how much the idea will cost and plan for the expenses.

However, the fixed price may reduce the flexibility of the team. Once the team has agreed on the scope and cost, changes are possible only on a new agreement. As a result, the conversation restarts every time a startup comes up with an improvement. The team should estimate the new scope taking time and resources. Working on a fixed-price basis can slow down development every time project requirements change – and they change constantly.

The time-and-material model is a better option for startups. The development team can flexibly change to new priorities without needing to agree on new terms.

2. Reduce Where Possible—But Wisely

A dependable team invests efforts in providing accurate estimation of the cost of startup development. However, the estimate may exceed the expected budget as a result of the large project scope and the risks posed by the many unknowns.

Discuss the results and determine what you can reduce without compromising on product quality. The following points are included:

• Cutting all except key product features.

• Using frameworks and ready-made modules.

• Application for basic design.

• Explaining project details with the help of presentations, clickable prototypes, demos or proofs of concept.

Some decisions that can reduce the bottom line include giving up essential activities (eg, DevOps or QA), expanding the scope of a fixed-price project – as opposed to a previous agreement – and including one with fewer project hours. Including offering third-party estimates.

3. Hire an expert team and invest in collaboration

To reduce development time, it makes sense for non-technical startup founders to bet on expertise. This may mean hiring someone with a technical management background as the CTO and then building a team with their help. It may also mean hiring a salesperson team with proven experience in launching startup products, including a project manager and a business analyst.

Both options have their advantages and disadvantages, but in any case active participation in software development is necessary. To stay on track, startups must regularly discuss their plans and priorities with the engineering team. In turn, the team of professionals can suggest optimal implementation for what is learned from the marketing study.

The right expert should be able to explain the terms and concepts of software development in simple terms if you ask them to.

4. Focus on Product Architecture

Two factors influence product architecture: the feature list and the number of (future) users.

At first, the initial idea would change several times until the startup found the right formula. You’ll add new features, update existing features, and remove irrelevant ones to test market interest. You need a flexible architecture to manage changes effectively, thus avoiding major changes.

Second, the flexible architecture lets you maintain the best balance between maintenance cost and user experience. On the one hand, spare capacity is expensive. You need to spend less on infrastructure when there are only a few users. On the other hand, when popularity grows rapidly you need to scale rapidly.

Founders will need to ensure that the team has an architecture in place that can enable both keeping costs reasonable today and supporting future growth plans.

5. Build Using a Popular Tech Stack

The popularity of the technology is another concern in software development apart from the experience of the developers working with it. Choose widely popular languages, frameworks and libraries when all other things are equal. Evaluate the following parameters:

• Availability of launched projects similar to yours.

• Regularity of updates.

• A large, vibrant community around the technology.

• Support of a corporation or a foundation.

These parameters can help ensure that the technology is ready for long-term use. It will likely be available, stable and secure in the future.

Another reason for using extensive techniques is ease of replacement. Startups using an unpopular technology face the risks of increased vendor dependency in the case of outsourcing or a higher bus factor in the case of internal development.

6. Take security challenges seriously

Cyber ​​criminals target startups of any size. Potential targets include produced source code, software infrastructure and perimeters, project participants and their equipment, and end users’ accounts.

Startups can only feel secure when they implement strict security policies for internal processes (including software development workflow and data exchange between team members), storage and processing of user data, and compliance with data protection laws.

Designing a secure software architecture is essential. Check source code and infrastructure for vulnerabilities and close them quickly. Make sure team members have relevant permissions and can only access the information needed for the job. Educate users on how to protect against phishing. Freeze suspicious and hacked accounts immediately to prevent large scale attacks.

Final thoughts

Running a digital startup for the first time requires the founder’s concentration on activities they may not have done before. While a seasoned startup can launch faster, a first-time startup can also create a market-fit product within a reasonable amount of time. This is possible when your startup is agile, invests in aspects that enable long-term improvements and incorporates the expertise that engineers bring to your project.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource: https://biz.crast.net/what-startup-founders-need-to-know-about-software-development/

The Role Of Data In The Metaverse

 In his sci-fi novel Snow Crash, Neal Stephenson introduced the word “metaverse” all the way back in 1992. In the novel, the metaverse is a computer-generated universe for its protagonist Hiro, amplified by his goggles and headsets. And ever since Facebook changed its name to Meta and CEO Mark Zuckerberg effectively bet his company’s future on the metaverse, the hullabaloo surrounding the term only seems to be increasing daily. But what is the metaverse?

The Covid-19 pandemic forced many of us to work from home, taking part in meetings via Zoom and interacting will our colleagues over Slack. But what if we can create a more immersive world? A world where we can teleport ourselves somewhere to interact with our friends, family and colleagues without physically being present there? Imagine being able to work, learn, play, shop, create and do so many other things that we cannot do just using our computers and mobile phones. I believe the next version of the internet will offer these experiences in a combined virtual space that is the metaverse.

Now, metaverse is a universal word used to describe a combined virtual space, but there are specific technologies that comprise it. Augmented reality (AR), virtual reality (VR), head-mounted displays (HMD), the Internet of Things (IoT), 5G, artificial intelligence and spatial technologies are some of them. These technologies, when combined, offer the complete metaverse experience. However, in order to provide that experience, these technologies will need to rely on data to a large extent, particularly good data (i.e., data with integrity). This article will attempt to discuss the role of data in the metaverse and how some of the aforementioned technologies can leverage that data.

One of the key elements of the metaverse is using avatars to represent ourselves. Our avatars would be similar to our profile pictures that we use on social media sites, but not the static ones that we currently use. Instead, they would be dynamic, 3D representations of us. And we might have multiple avatars—one for gaming, one for our professional lives, one for hanging out with our friends, etc.

App creators would be able to offer us these avatars in the metaverse, but how would they create them? AI, one of the metaverse technologies I previously mentioned, would play a critical role in designing these customizable avatars. AI can analyze 2D user images or 3D scans to create realistic and accurate avatars. We’re already seeing this play out in the real world with apps like Lensa, which takes users’ profile pictures and uses AI to convert them into digital avatars. And as part of the work that we do for our clients at Zuci Systems, we are exploring the possibility of AI being able to create models that provide suggestions for user avatars for our healthcare clients.

Another element of the metaverse that would rely on data significantly is the use of digital twins. These are digital representations of physical infrastructure and devices that data scientists and IT professionals can use to run simulations on. Digital twins are continuously updated with historical as well as current data from various sources to create an up-to-date representation as soon as new information becomes available.

At our company, we are in the early stages of using digital twins for one of our large clients, but we can already see how digital twin technology would have an impact on the AR and VR components of the metaverse. The use of digital twin technology can come in handy for metaverse app creators to test various scenarios or specific settings before actually implementing them as part of the metaverse.

A recent McKinsey article notes that digital twin technology will play a key role in helping shape the “enterprise metaverse.” The article states: “Ultimately, the ‘enterprise metaverse’ will be powered by dozens of interconnected digital twins that replicate everything from physical assets (like products and office buildings) to people (such as customers and employees) to core business processes and often interact with the physical environment without human intervention.” One can only imagine the amount of data in a scenario like this.

The technologies mentioned here are certainly going to play a critical role in the development of the metaverse. And needless to say, high-quality training data will be required so that developers can use these technologies to build the platforms and apps that will allow the metaverse to reach its full potential.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource: https://www.forbes.com/sites/forbestechcouncil/2023/01/30/the-role-of-data-in-the-metaverse/?sh=5e0ac567792f

Data And AI Will Redefine Businesses In 2023

 As we launch into 2023, the world is in the middle of three very large transitions. First is the transition to a new geo-political, social-economic work order. Second is the transition to a low-carbon economy. And third is the first truly scaled deployment of AI in the enterprise. And across these opportunities and challenges, a few key technology trends are clearly taking shape: