6 Ways AI Transforms How We Develop Software

 AI is transforming all business functions, and software development is no exception. Not only can machine learning techniques be used to accelerate the traditional software development lifecycle (SDLC), they present a completely new paradigm for inventing technology.

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

What’s The Difference Between Machine Learning And Artificial Intelligence, Anyway?

 - Artificial intelligence is the ability of a computer to handle complex tasks such as learning and problem-solving

- Machine learning is a computer application using artificial intelligence to find patterns and trends in complex data sets without human instructions

- The trend of AI and ML in business and beyond will likely accelerate as public AI websites and tools draw attention to potential uses

Achieving Next-Level Value From AI By Focusing On The Operational Side Of Machine Learning

Technology research firm Gartner, Inc. has estimated that 85% of artificial intelligence (AI) and machine learning (ML) projects fail to produce a return for the business. The reasons often cited for the high failure rate include poor scope definition, bad training data, organizational inertia, lack of process change, mission creep and insufficient experimentation.

To this list, I would add another reason that I have seen many organizations struggle to achieve value from their AI projects. Companies often have invested heavily in building data science teams to create innovative ML models. However, they have failed to adopt the mindset, team, processes and tools necessary to efficiently and safely put those models into a production environment where they can actually deliver value.

To avoid this trap and achieve greater value from AI, here are four recommendations to help your organization translate your data scientists' amazing algorithms into real business impact.

1. Adopt a software mindset.

ML models are undoubtedly important, but developing ML code is just one part of the AI/ML life cycle. Data collection, feature extraction, data verification, machine resource management and other activities adjacent to the ML code actually consume the bulk of time and resources in the ML life cycle.

To be successful, companies must stop thinking of models as an end on their own. The fact is that a model is just a way to transform data written in the form of a function. In short, the model is just software.

When software engineers think about putting a model into production, their concerns are around how the model handles errors, whether the model will do what it is expected to do, whether it can respond quickly enough and whether it will integrate effectively into the organization's software stack.

Adopting a software mindset means moving away from an "artisanal" approach of handling every model as a one-off toward an "industrial" approach focused on putting the tools and processes in place to get models into production efficiently and effectively.

2. Build an ML platform team.

Since models are software, companies should look to their software organizations when they think about how to structure the ML operations team that will be responsible for bringing models into production.

Where a software organization has product development teams supported by an applications platform team (along with a core group to manage the infrastructure), the AI/ML organization should have data science teams supported by an ML engineering group—along with a team whose mandate is to assemble, manage and monitor the platform that the data science and ML teams use (i.e., an ML platform team staffed with ML platform engineers).

The ML platform engineer is a crossover role—similar to a DevOps position, plus software since they might need to build APIs or support the development of infrastructure patterns, for example. Awareness of data helps because data is so intertwined with ML. The ML platform engineer role also requires strong soft skills, curiosity and a collaborative mindset since they will work with diverse teams across the ML life cycle.

3. Establish end-to-end processes.

When a company is still in the "artisanal" stage of ML and is working with only a few use cases, it can get by with bespoke processes, treating each model as a one-off. However, as it expands the number of models that it's putting into production, it needs to standardize its processes to ensure a high level of confidence in both the processes themselves and in the models that it's putting into production.

This means establishing processes across the entirety of the model life cycle—which can be challenging because of the diverse teams involved throughout the life cycle. For example, different groups or individuals tend to be involved in promoting models from lab to staging and then to prod. As a result, different processes need to be implemented for each stage.

It's worth saying again that processes need to be established across the entire model life cycle. Yes, handoffs need to be defined all the way from experimentation to production. However, a model's life cycle doesn't end when it goes live in production, and procedures should be vetted for monitoring and retraining models as well.

4. Incorporate an operational platform.

Many companies that are successful with AI/ML invariably have a dedicated platform for operationalizing models for a variety of reasons. First and foremost, the computational workloads that a system supports in experimentation or training are very different from the workloads in the operationalizing phase.

In experimentation, the limiting factor is how quickly you can spin up resources independently so that you can use your Scikit-learn or TensorFlow and so on. When you go into the implementation phase, you care about a completely different set of capabilities. Is the platform resilient and high availability? Does it have hooks into Datadog or New Relic?

That's why even companies that have a training platform should consider incorporating an operational platform. As a rule, the ML platform itself should provide "self-service with guardrails," allowing data scientists to quickly and safely deploy models into production. At a minimum, the tools that a high-functioning ML team requires for managing operational AI workloads at scale should include:

• A training platform.

• An operational AI (or serving) platform.

• A data platform.

• DevOps to orchestrate everything.

• A workflow system, which may or may not include a batch prediction platform.

By adopting a software mindset around ML and putting in place the team, processes and tools to safely and efficiently deploy ML models, companies can significantly reduce the time required to put models into production and see value from their research innovations.

Implementing standard end-to-end processes can also improve model governance and prepare teams for upcoming regulations around AI, such as the EU's AI Act and the American Data Privacy and Protection Act (ADPPA).

Finally, these companies can free up their data scientists to develop even more innovative models to deliver intelligent products and services, ultimately increasing AI's value and impact on the business.

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/2023/01/17/achieving-next-level-value-from-ai-by-focusing-on-the-operational-side-of-machine-learning/?sh=22fd8f682d7e

Machine Learning Enables Inclusive Access To Financial Services

 Technology in financial services can be somewhat of a double-edged sword. On one side, new technological innovations, like artificial intelligence (AI) and machine learning (ML), are striving to make financial products and services more available by making it easier to identify customers, expedite credit approval, and improve access to financial services for all. On the other side, if the proper precautions are not taken, companies in the industry might be using data to power AI applications that hold intrinsic biases, which work against efforts to make technology more inclusive and accessible.

Complete guide to Machine learning in the media industry

For the past few years now, the increasing digitalization of customer journeys and the exponential improvement of cloud technologies and computing capacities have invited media groups to rethink the way they do business. If it sounds like I’m using long words to say “digital disruption”, trust your instinct. Many of these disruptions have been centered around the mountains of data media groups have access to, and what Artificial Intelligence (AI) (and machine learning more specifically) could do with it. Indeed, while artificial intelligence has been fully embraced by a plethora of pure players (Spotify, Netflix, Buzzfeed, Disney…) traditional actors are still lagging, and now see the technology as a shortcut to a much-needed renewed growth. 

Machine Learning for Data Management

Data impacts nearly every part of our lives and is critical for companies to stay relevant. It has transformed almost every industry to drive efficiency, better insights, and business growth.

However, managing this data can take an enormous amount of time and expense. Between security, auditing, organizing, and more, managing data sets can create a significant strain on employee time and energy. In fact, most data scientists and business analysts spend around 80% of their time finding, cleaning, and reorganizing data sets. This leaves just 20% of their time to spend on value-generating activities.

3 Ways In Which Machine Learning Streamlines Corporate Restructuring

 

The criticality of getting corporate restructuring right is hard to overstate. As you may know, restructuring is normally carried out when an organization is not in the best financial health. A complete overhaul of existing working methods and the overall structure of an organization to avoid financial crises and stabilize business performance necessitates the proper extraction and use of data and resources. Corporate restructuring involves adhering to a robust business strategy while carrying out SWOT analysis, creating new strategies for the future, adding and eliminating operations and resources depending on financial requirements and launching a new brand language, if necessary, to turn the fortunes of a failing business around.

The Future Of Work: How Slack Is Reinventing Hybrid Collaboration With Machine Learning

That virtual meeting that could have been an email? It should have been a Slack Huddle or a clip.

Slack, an already integral part of the modern workforce, is redefining the norm of remote and hybrid work yet again with its new collaboration tools - huddles and clips. With the help of AWS machine learning (ML), the company has reinvented collaboration across time zones in an increasingly isolating hybrid workplace. In just a few short months, Slack has leveraged ML to innovate, improve products, and speed time to market on the cloud.

Machine learning reveals brain networks involved in child aggression

The study revealed brain hubs (dots) and connections (lines) predictive of aggressive behavior. (Credit: Ibrahim et al. 2021)

Child psychiatric disorders, such as oppositional defiant disorder and attention-deficit/hyperactivity disorder (ADHD), can feature outbursts of anger and physical aggression. A better understanding of what drives these symptoms could help inform treatment strategies. Yale researchers have now used a machine learning-based approach to uncover disruptions of brain connectivity in children displaying aggression.

While previous research has focused on specific brain regions, the new study identifies patterns of neural connections across the entire brain that are linked to aggressive behavior in children. The findings, published in the journal Molecular Psychiatry, build on a novel model of brain functioning called the “connectome” that describes this pattern of brain-wide connections.

“Maladaptive aggression can result in harm to self or others. This challenging behavior is one of the main reasons for referrals to child mental health services,” said Denis Sukhodolsky, senior author and associate professor in the Yale Child Study Center. “Connectome-based modeling offers a new account of brain networks involved in aggressive behavior.”

For the study, which is the first of its kind, researchers collected fMRI (functional magnetic resonance imaging) data while children performed an emotional face perception task in which they observed faces making calm or fearful expressions. Seeing faces that express emotion can engage brain states relevant to emotion generation and regulation, both of which have been linked to aggressive behavior, researchers said. The scientists then applied machine learning analyses to identify neural connections that distinguished children with and without histories of aggressive behavior.

They found that patterns in brain networks involved in social and emotional processes — such as feeling frustrated with homework or understanding why a friend is upset — predicted aggressive behavior. To confirm these findings, the researchers then tested them in a separate dataset and found that the same brain networks predicted aggression. In particular, abnormal connectivity to the dorsolateral prefrontal cortex — a key region involved in the regulation of emotions and higher cognitive functions like attention and decision-making — emerged as a consistent predictor of aggression when tested in subgroups of children with aggressive behavior and disorders such as anxiety, ADHD, and autism.

These neural connections to the dorsolateral prefrontal cortex could represent a marker of aggression that is common across several childhood psychiatric disorders.

“This study suggests that the robustness of these large-scale brain networks and their connectivity with the prefrontal cortex may represent a neural marker of aggression that can be leveraged in clinical studies,” said Karim Ibrahim, associate research scientist at the Yale Child Study Center and first author of the paper. “The human functional connectome describes the vast interconnectedness of the brain. Understanding the connectome is on the frontier of neuroscience because it can provide us with valuable information for developing brain biomarkers of psychiatric disorders.”

Added Sukhodolsky: “This connectome model of aggression could also help us develop clinical interventions that can improve the coordination among these brain networks and hubs like the prefrontal cortex. Such interventions could include teaching the emotion regulation skills necessary for modulating negative emotions such as frustration and anger.”

Other Yale authors included Stephanie Noble, George He, Cheryl Lacadie, Michael J. Crowley, Gregory McCarthy, and Dustin Scheinost. Funding was provided by the National Institute of Mental Health and the Yale Child Study Center Faculty Development Fund.

Article Source: https://news.yale.edu/2021/10/26/machine-learning-reveals-brain-networks-involved-child-aggression

The Future of Low-Code is Open

 The Future of Low-Code is Open

The low-code market is seeing meteoric rise across the world, as companies try to keep up with digitization demands and shrinking IT budgets. Even as we witness increasing low-code adoption among professional as well as citizen developers, an intriguing question comes to mind – What lies ahead for low-code, and could it ever become a mainstream approach for modern development teams?

Artificial Intelligence And The Future Of Humans

Artificial intelligence is already impacting virtually every industry and every human being. This incredible technology has brought many good and questionable things into our lives, and it will create an even bigger impact in the next two decades.

AI (Artificial Intelligence): Build It Or Buy It?

AI Champions Driving New Industry Solutions For Climate Change

AI Champions driving new industry solutions for Climate Change GETTY

Climate change is the planet’s greatest challenge. The UN has already stated that 2021 is the final year for us to make real change in the fight against rising global temperatures. 

Scientists create online games to show risks of AI emotion recognition

Scientists create online games to show risks of AI emotion recognition

It is a technology that has been frowned upon by ethicists: now researchers are hoping to unmask the reality of emotion recognition systems in an effort to boost public debate.

Big data trends to consider in 2021

Big data trends to consider in 2021

Intro

Big Data is keeping up with the pace. According to some studies there are 40 times more bytes in the world than there are stars in the observable universe. There is simply an unimaginable amount of data being produced by billions of people every single day. The global market size predictions prove it beyond any doubt.

The Dumb Reason Your AI Project Will Fail

The Dumb Reason Your AI Project Will Fail

Here is a common story of how companies trying to adopt AI fail. They work closely with a promising technology vendor. They invest the time, money, and effort necessary to achieve resounding success with their proof of concept and demonstrate how the use of artificial intelligence will improve their business. 

How to become a machine learning engineer

How to become a machine learning engineer

What’s involved in being a machine learning engineer and how to become one

An AI Engineer Walks Into A Data Shop...

An AI-focused neural network software engineer walks into a data shop says hello to the shopkeeper. “I’ll have two data preparation functions, one testing and debugging toolset, a couple of application log tracking systems and a bag of potatoes,” asks the engineer.

What Robots Can Do for Retail

What Robots Can Do for Retail

Robots have rolled into retail, from six-foot-tall free-moving machines spotting spills in Giant Foods Stores to autonomous shelf-scanners checking inventory in Walmart. At Lowe’s, the home improvement chain, a “LoweBot” in some stores can answer simple questions, such as where to find items, and can assist with inventory monitoring. These robots free up workers from routine tasks, presumably giving humans more time for customer interaction — but that’s only the beginning.