1.     Limited Technical Understanding 

Given the vastness of software architecture, it is difficult to grasp how inefficiencies impact cost and operations. To combat this, view your company’s software as an evolving system rather than a fixed asset. 

2.     Lack of Measurable ROI 

Unlike revenue-generating initiatives they generate revenue immediately and expand market share, efficient software oftentimes takes a back seat to more measurable business objectives. 

3.     Cost vs. Long Term savings 

Improving software efficiency sometimes requires upfront investments in infrastructure, developer time, or new tool implementation. It is easy to shy away from these investments as long-term savings are not immediately visible. 

At Valore Partners, we understand that as a business leader, juggling multiple critical aspects of your company can be challenging. To help you grasp the significance of efficient and effective software solutions, we’ve created a concise, jargon-free article that simplifies key insights and enhances your understanding of software’s role in business success. 

Maximizing Developer Productivity for Business Growth 

The Cost of Inefficient Code 

The Consortium for Information and Software Quality estimates that poor software quality in the US has grown to $2.41 trillion, and your business could be contributing to that growth. Poor code leads to slow applications which frustrate employees and customers and hinders project performance. Often, decision makers make the mistake of investing in costly infrastructure updates to keep a bloated and inefficient system running. Poorly written code slowly accumulates technical debt, leading to higher maintenance expenses and slower development cycles. To combat this, our experienced developers follow the best coding practices that ensure that software is maintainable and scalable. Our professional team ensures clean architecture and proper documentation, making future updates and maintenance easier and more cost effective for your growing business. 

Agile Development 

Very often is the word “Agile” tossed around the tech industry, but what does it really mean? Agile development is a flexible and iterative approach to software development that prioritizes collaboration and continuous improvement, allowing your teams to quickly respond to changes and deliver high-quality products more efficiently. 

 Traditional (Waterfall) development follows a strict-step-by-step process where each phase must be completed before moving to the next. Any modifications to the software typically need to be formally approved, further causing delays. Testing the software for fixes and modifications only occurs at the end, making it harder to detect and fix issues early. Contrastingly, Agile development provides for high flexibility and cross functional collaboration across teams, all while helping you and your team stay ahead of the competition. 

Partner with Valore Partners for Smarter Software Solutions 

Investing in efficient, scalable, and well-architected software is not just about cutting costs, it’s about driving growth, improving productivity, and staying competitive in an ever-evolving market. By embracing Agile development and prioritizing clean, maintainable code, your business can reduce technical debt, accelerate time-to-market, and enhance customer satisfaction. 

At Valore Partners, our team of expert developers is ready to help you optimize your software, streamline operations, and unlock new opportunities for success. If you’re ready to transform your business with high-performance, future-proof software solutions, contact us today or visit our website for more information at valorepartners.com. 

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What is Dynamics 365 Finance?

Dynamics 365 Finance is a comprehensive platform designed to streamline financial operations through a range of features including:

• Cash Management
• Business Performance Management
• Tax Management
• Quote to Cash
• Accounting and Financial Close
• Financial Planning and Analysis

These features are supported by live data integration across various business functions such as human resources, sales, and supply chain. Microsoft’s AI, Copilot, then augments this seamless data integration by providing automated forecasting and predictive insights.

Common Challenges in Extracting Relevant Reports

Despite the features provided by Dynamics 365 Finance, extracting relevant and actionable reports can remain challenging without strategic use of the platform.

1. Complex Data Integration: While integrating data from multiple business functions can be beneficial, extracting the information into a cohesive report can be complex. It is important to carefully choose which data sources to include, and how to structure that data to your business specific needs.
2. High Customization: The extensive customization provided can come in as a double-edged sword. While customization allows for tailored reports, it is important to know what customization is necessary for your specific industry. If not, unnecessary customization can increase costs and increase the delay to receiving valuable reports.

Leverage a Few Best Practices

To help navigate the complexities of report generation and make the most of this powerful platform, consider implementing the following best practices tips. These approaches are designed to help mitigate the above challenges and ensure effective use of Dynamics 365 Finance Reporting and Analytics.

• Prioritize Data Quality and Structure: Ensure data is clean and well-organized to generate accurate reports. Collaborate with the IT team to minimize data inaccuracies and performance issues, especially when integrating data from external applications outside of Dynamics 365 Finance.
• User-Friendly Reporting Interfaces: Design accessible reports and dashboard layouts, while providing training to reduce dependency on IT. This empowers end-users to access and interpret key metrics without waiting. While also providing IT time to be spent on other priorities for smooth reporting operations.
• Leverage Automation and Scheduling: Take advantage of the many automation features that are innately found in Dynamics 365 Finance. By taking the time to configure this, it helps reduce the risk of human error while ensuring the business leaders receive relevant information without requesting it manually. This supports agile decision-making and streamlines operations.

Analytics and Reporting Tools in Dynamics 365 Finance

To address these challenges and utilize the approaches, it is important to leverage the core reporting tools within Dynamics 365 Finance:

1. Power BI (Business Intelligence)

Power BI is one tool that is seamlessly integrated with Dynamics 365 Finance. Power BI enables the creation of custom dashboards and reports from live data. This tool also supports the integration with external data sources, offering even further customization for businesses specific needs.

However, Power BI and Dynamic 365 Finance are complex tools to use. The technical skills required for effective usage of this tool are skills in data modeling and Dynamics 365 Finance configuration.

2. Electronic Reporting (ER) Framework

The ER framework excels in producing regulatory-compliant financial reports and can export in formats such as Excel or XML. It offers easy configuration without requiring coding, supports automation to report generation, and flexibility in reporting structure depending on business needs.

While strong in document formatting, it lacks advanced data visualization compared to other tools. These reports may also need ongoing maintenance whenever there are changes made in data structures or reporting adjustments, which could be resource intensive.

3. Financial Reporting (Management Reporter)

Financial Reporting excels in generating detailed financial reports, gathered through live data from Dynamics 365 Finance. This tool is much simpler to use compared to the previous tools we discussed and allows users to generate reports without the help of their IT team. It also includes flexible reporting design and has the option for automatic report generation based on configuration.

It is specifically designed for financial reporting and relies solely on general ledger data. It does not offer advanced analytics or visualizations, which may limit its usefulness for broader data analysis.

Conclusion

By understanding and addressing the challenges of generating reports and analytics, businesses are taking the first steps to utilizing the potential of Dynamics 365 Finance. Implementing the best practices and picking the right tools specific to your business needs are crucial. To maximize these efforts, consider partnering with an experienced implementation provider to enhance your efforts. A knowledgeable partner can offer specialized support, ensure seamless integration, and optimize the platform to your enterprise needs.

Here at Valore Partners we excel in connecting disparate systems and streamlining operations for our customers. To learn more about how we can help you achieve these goals, reach out to us today.

The post Business Insights: Leveraging Analytics and Reporting with Dynamics 365 Finance appeared first on Valore Partners.

Test-driven development (TDD) is a software methodology that emphasizes writing automated unit test cases before writing actual code. Once the intended test is set-up, developers then write the minimum amount of code to pass the test case. This is quickly followed-up with developers cleaning the code to have better design principle. This methodology differs from the traditional approach to coding by combining writing code and tests at the same time.

TDD does this by following short iterative development cycles known as “Red-Green-Refactor.”

• Red: This is setting up the blueprint for the intended code. Developers make a unit test case for a specific function, and then run it to make the test fail.
• Green: The green phase is where you implement code to make your test pass. Developers write a simple code that passes the test, regardless of the code design.
• Refactor: This is the highlight of the three-part stages, as developers refine their code into adhering to best design practices. The process then repeats itself until the project is complete.

TDD iterative approach naturally supports agile practices, with each development cycle regularly delivering functional software. By emphasizing test cases upfront, TDD delivers robust code that has the desired function while remaining adaptable.

Alternatively, there is a variation to TDD called Acceptance Test-Driven Development (ATDD). This approach adopts a more collaborative mindset between customer, developer, and tester. These three differing perspectives all have the end-user in mind and promote a product that meets the user’s needs.

In this introductory article to Test-Driven Development, we will explore implementing practices, benefits, and challenges that come with this software approach.

How To Implement Test-Driven Development

Test-driven development is a great software methodology that brings many benefits, but a deep understanding of its best practices is needed to utilize it fully. This section of the article will cover some best practices advice from one of our engineers at Valore Partners, Mark Scott.

Mark starts off by stating that teams adopting this methodology will need to make a time investment upfront. The main priority being to write well-written and clear software requirements for the initial test cases before writing any product code. He understands that many teams will see that implementing TDD requires too much time commitment. They would be correct when considering it in the short-term. The initial investment does require patience and increased effort up front; however, he argues teams will recapture this in the form of increased productivity in the long run.

Mark continues that teams must keep tests relevant and up to date as the base code evolves. This can be challenging in agile working environments, where there’s an emphasis on delivering projects quickly. One way to mitigate this challenge is to leverage professional tools, specifically through Continuous Integration/ Continuous Deployment (CI/CD) methodologies. This can help automate the testing process whenever changes to codes are made, maintaining the higher standard of code quality.

Test-Driven Development Benefits

TDD offers many benefits, making it one of the top methodological approaches to coding.  Some examples of said benefits include:

• Less Defects: By providing strong initial test cases, it will help detect issues early in the development cycle. This helps reduce time and effort spent on debugging later in production
• Improved Code Quality: The iterative nature and testing encourage developers to write modular and testable code, allowing easier rewrites and maintenance
• Enhanced Collaboration: TDD requires developers, testers, and stakeholders to remain on the same page when producing the test cases. This ensures all parties intent remain align during the project
• Flexible Code: As the base code evolves or when requirement changes, developers have the safety net of test cases in ensuring the code function is correct

Challenges of Test-Driven Development

While there are many benefits of using TDD, there are also as many challenges. It’s worth remembering that this is only a methodology to approach software, not the only right way of doing so.

• Initial Time Development: The time spent producing code for the project is instead spent on producing test cases. If the tests cases too complex, it will slow down development time in the initial phase
• Limited Test Coverage: TDD specifically focuses on unit level testing, but it remains important to incorporate other types of testing such as integration, performance and end-to end scenario testing
• Maintenance Overhead: TDD relies on having good test cases to ensure the project develops smoothly, which can lead to maintenance overhead while attempting to keep tests updated as code changes

Conclusion

With proper implementation of test-driven development, software projects will have stronger code design compared to the traditional approach. It’s important to remember while adopting this methodology to have an open discussion with the team to ensure everyone is on the same page. This will lead to deciding on the correct software tools to quickly deliver projects using test-driven development, and to produce reliable test cases for reliable software architecture.

The post An Introduction To Test-Driven Development appeared first on Valore Partners.

Article 2 of 2 – An overview of Zero Trust and its Capabilities

While an Identity and Access Management (IAM) system is crucial for a business’s cybersecurity, ongoing reinforcement is essential to maintain its effectiveness. So, how are more and more companies ensuring that vital information stays secure and accessible only to the right people?

Background

The answer lies in the framework of Zero Trust Architecture (ZTA), a security framework that assumes no user, device, or network is inherently trustworthy. The concept of Zero Trust evolved over time and its need was rooted in the requirement for more robust cybersecurity. The early 2000s marked the time when experts began questioning the traditional cybersecurity methods of corporate VPNs. In 2009, John Kindervag, an analyst at Forrester Research, formally introduced the Zero Trust model. As the years followed and organizations began experiencing more sophisticated cyber threats and data breaches, the Zero Trust model began gaining traction.

With growing fears of cyber security attacks, even the Department of Defense (DoD) unveiled its Zero Trust strategy in 2022 to bolster its cybersecurity posture. Additionally, a year prior to that, President Biden directed all US Federal agencies to implement a zero-trust architecture.

Zero Trust Principles

Business Enablement

Align security to the organization’s mission, priorities, risks and processes. Employees can securely access essential resources from any location, enabling remote work and enhancing collaboration without compromising security. Additionally, Zero Trust allows businesses to allocate security resources more efficiently, concentrating efforts on high-risk areas and minimizing unnecessary expenditures on broad security measures.

Assume Breach

ZTA assumes attackers can and will successfully attack anything (identity, network, device, infrastructure, etc.). By assuming a breach, Zero Trust adopts a proactive security stance, identifying and mitigating threats before they cause significant damage. This constant verification reduces the number of weak spots available to malicious sources.

Verify Explicitly

Protect assets from attacker control by explicitly validating that all trust and security decisions incorporate all relevant available information.

Use Least-Privilege Access

Restrict access to potentially compromised assets by using just-in-time (JIT) and just-enough-access (JEA) approaches, along with risk-based policies such as adaptive access control. This limited access granted to users reduces the potential entry points for attackers. Meaning that even If an attacker gains access to a user’s credentials, their reach within the network is limited.

In summary, Zero Trust principles are no longer just a recommended strategy, rather a necessity in today’s cybersecurity landscape. By focusing on verifying identities and managing access, IAM serves as the cornerstone of ZTA. Together, these approaches form a dynamic, adaptive defense that effectively secures critical assets.

The post Zero Trust and Identity Access Management: Lets Take it One Step Further appeared first on Valore Partners.

Identity Access Management (IAM) is crucial for the safe operation of any organization’s IT infrastructure, regardless of the industry. With the shift from traditional on-site work (outside a company’s firewall) to hybrid workspaces, the need for secure access to organizational resources, such as applications and data, has never been greater. IAM ensures that only authorized individuals and devices can access these resources.

How does IAM work?

Here are the three high-level components of IAM:

1. Identification

Individuals and devices are normally registered in an IAM system through an onboarding process in which they’re given a username or user ID.

2. Authentication

Authentication methods include passwords, biometrics, multi-factor authentication, or Single Sign-On (SSO) in which users can access multiple applications with one set of login credentials.

3. Authorization

Here, IT ensures that users are assigned roles that define their permissions and access levels within the system. Access Control Lists are a key component of authorization as these lists determine and manage who and what can access specific computing resources, and what actions can be performed. Think of this as a set of rules that define permissions attached to an object like files or directories. These rules then identify users or system processes that can interact with the objects and the operations they perform.

The importance of IAM cannot be overstated

Cybersecurity

The expected benefit of IAM is its ability to set controls that ensure that only authorized users and devices can access sensitive information, thus reducing the risk of data breaches and unauthorized activities.

Operational Efficiency

IAM automates many aspects of user lifecycle management, including provisioning, deprovisioning, and role management, significantly reducing the administrative burden on IT staff. Additionally, Single Sign-On (SSO) enhances user efficiency by allowing access to multiple systems with a single set of credentials, eliminating the need to manage multiple passwords.

Data Encryption

Integrating IAM and data encryption creates a powerful security framework. Microsoft notes that “elevating an organization’s security is that many IAM systems offer encryption tools.” This combined strategy not only strengthens data protection but also aids organizations in meeting regulatory requirements and industry standards.

In today’s rapidly evolving digital landscape, IAM systems are indispensable for safeguarding enterprise resources and ensuring operational efficiency. As organizations have transition to hybrid work environments, the importance of secure access to applications and data becomes paramount. By implementing robust IAM systems, companies can ensure that only authorized individuals access sensitive information, reducing the risk of data breaches and unauthorized activities.

The post Introduction to Identity Access Management Systems appeared first on Valore Partners.

From autonomous cars to virtual bots on websites, there have been more artificial intelligence applications appearing in our day-to-day lives. However, AI has emerged as an evolving field that has driven significant changes across multiple sectors. As our artificial intelligence series ends, we shift our focus to exploring lesser-known applications in different industries. We also speculate on the future advancements of artificial intelligence and thought it would be fun to use what we call the AI stages of grief to explain how organizations adapt to these changes.

AI Applications Across Different Industries

1. Education

Artificial intelligence is revolutionizing education in numerous ways. This is evident in how the educational sector is transforming traditional teaching and learning methods by integrating AI. Given the global teacher shortage, the integration of AI into education enables automated grading and streamlines administrative tasks, giving teachers more time for valuable student-teacher interaction and engagement. AI-powered tutoring systems tailor the learning to individual students that adapt to each student’s learning style. The benefits of 1:1 tutoring is widely recognized as tutoring can help strengthen subject comprehension and build overall learning skills. Tutoring, a once inaccessible tool to many students, is now available to more people due to AI.

2. Human Resources

One of the most helpful AI tools has been its application in the recruitment and selection aspect in the hiring process. Because reviewing hundreds of candidates’ resumes and applications is a tedious and tremendously time-consuming process, AI tools serve to automatically screen resumes and identify qualified candidates. Another helpful characteristic of AI integration is its use in benefits administration. AI tools streamline benefits package comparison, ensuring optimal coverage and affordability. They autonomously verify employee eligibility, freeing up HR for strategic planning.

3. Healthcare

One of the most anticipated AI advancements can be seen in healthcare and medicine. AI aids in population health by empowering individuals to self-manage chronic conditions like asthma as it connects individuals to relevant screening and therapy. All this while also providing reminders for medication adherence and other necessary care steps. Additionally, a recent Mayo Clinic cardiology study found that AI effectively detected individuals at risk of a left ventricular dysfunction. In other words, identifying those who may be at risk of suffering from a weakened heart pump without displaying noticeable symptoms. This is just one example of the growing synergy between AI and medical research.

4. Agriculture

Artificial intelligence in agriculture is revolutionizing the way we cultivate crops, manage resources, and optimize yields. Multimodal AI integrates diverse data sources to provide farmers with comprehensive guidance on crop selection planting schedules, fertilization timing and harvest timing. With tools like Microsoft’s FarmBeats, farmers can learn about soil and yield maps, weather data analysis and predictive analytics to help them make informed decisions about crop management. These innovations hold promise for addressing challenges such as feeding and growing global population and mitigating the impact that climate change has on agriculture.

5. Finance

AI technologies empower financial institutions to automate tasks, analyze vast data sets, and make informed, data-driven decisions. This enhances operational efficiency and diminishes the risk of overlooking illicit fund flows. AI-powered surveillance further facilitates regulatory compliance by identifying trades that may contravene regulations. Moreover, AI enables intricate financial analysis and risk modeling by detecting correlations in extensive data sets that surpass human capacity. Consequently, it fosters informed decision-making in areas such as investments, lending, and insurance underwriting.

Future Impact and Implications

Generative AI has notably popularized artificial intelligence, demonstrating its value to businesses. However, throughout this article, we can see that artificial intelligence has an impact in many ways. In the IBM 2023 Survey, about 42% of enterprise-scaled companies report to have actively deployed using AI.

As such, it comes to no surprise that the demand for roles in artificial intelligence will rise. A few examples of said jobs are machine learning specialists, data scientists, or information security analysts. However, OpenAI supported by Microsoft, is conducting research on artificial general intelligence (AGI).

Amazon defines AGI as a field of theoretical research that attempts to create software with human-like intelligence and the ability to self-teach. The aim is for the software to be able to perform tasks that it is not necessarily trained or developed for. Although still in its initial stages, breakthroughs could potentially revolutionize the job market due to automation.

Another exciting development is the advancement of augmenting humans with implantable brain-computer interfaces (BCIs). These BCIs are designed to connect the human brain directly to computers, opening communication between the brain and external devices.

The leading pioneer in this is the company Neuralink, a neurotechnology company founded by Elon Musk. A part of researching this is to help those who are paralyzed such as Noland Arbaugh, who the first person to receive it around Jan 2024. Elon Musk real intention, however, is to have symbiosis with humans and AI.

With recent success, BCI is paving the way towards new developments in enhancing humans by integrating the capabilities of AI. While promising, BCIs also raise concerns about technological inequality due to high costs and access disparities.

Adapting to AI: Stages of Grief

With AI becoming an integral part of various industries, business leaders face the challenge of navigating an evolving technological landscape. The development that artificial intelligence is heading down naturally brings a range of emotional and practical responses. This can be because of the effect it could have on the job market to the effect it has on an individual level.

This section explores the stages of grief as we transition from traditional work settings to a future increasingly defined by AI. By examining the stages of denial, anger, and acceptance, organizations can make strategic decisions for a successful adaptation to AI.

Denial: Many individuals express apprehension about AI’s capabilities. The World Intellectual Property Organization noted that “the undirected use of AI could lead to substantial job losses in the short term.” Companies are not investing in AI as much as they should be due to a lack of understanding, fueling fear and skepticism.

Anger: Fear, mistrust, and resistance towards AI stem from factors such as the loss of human touch and ethical concerns. The Wall Street Journal highlights an incident with Levi Strauss & Co., which faced backlash for testing AI-generated images of diverse models, leading critics to argue it deprived real models of employment opportunities.

Acceptance: While concerns about AI replacing human workers are common, the reality is quite different. Change is necessary for progress but often brings fear and resistance. Preparing the workplace and addressing concerns can ease this transition. Encouraging a culture of collaboration between AI and human workers is essential for technological advancement.

Conclusion

In summary, artificial intelligence is transforming diverse sectors, from agriculture to finance. Its impact extends beyond just enhancing business operations; it also opens exciting future possibilities for AI. By exploring the stages of grief, organizations and individuals can gain valuable insights into the challenges and adjustments involved in adopting AI. With a strategic approach, organizations can navigate this transition smoothly and fully capitalize on the benefits AI offers.

The post Shaping the Future: How AI is Revolutionizing Industries and What’s Coming Next appeared first on Valore Partners.

As we continue this series, this article aims to discuss the current state of the artificial intelligence landscape.

Artificial Intelligence Development

When examining the current state of artificial intelligence, let’s first look at the current state of development for artificial intelligence. After examining the history of artificial intelligence in our previous post of this series, we can see that many of the advancements in this field have been done in an academic sense.  Nowadays, artificial intelligence has moved away from only being developed in labs to now also being developed by businesses. According to Standford’s 2024 AI Index Report, industries take the lead with 51 notable machine learning models versus academia only contributing 15. Nvidia, Microsoft, and OpenAI are a few major contributors to artificial intelligence development as of 2024.

This can be contributed to the accessibility of artificial intelligence to the public, whether it be individuals or businesses. The accessibility to the development of artificial intelligence has transformed many industries such as healthcare, finance, and automotive manufacturing.

Role of Artificial Intelligence

With businesses now taking the mantle of developing artificial intelligence, business leaders now have more tempered expectations on the role artificial intelligence plays in their field. In part, this is due to artificial intelligence now being adopted into the workplace. While standalone tools like ChatGPT take the spotlight in today’s world of being revolutionary, businesses each have different takes on how they approach implementing AI.

In fact, IBM states that enterprise environments are looking at high-impact generative applications that enhance or integrate into their systems instead of replacing or revolutionizing. This can be seen by whether they develop their own in-house generative model or leveraging tools like “Copilot” in Microsoft Office.

Generative AI, however, isn’t the only artificial intelligence that has integrated into current society. Other pivotal subfields that contribute to the society we see today are machine learning, robotics, natural language processing, and computer vision. Some examples are cashier less-checkouts, website chatbots that offer advice, and personalize content in streaming platforms or social media due to better optimize algorithms.

Artificial intelligence offers a variety of benefits in many industries, but there also come new challenges with it.

AI Challenges & Issues

From the Standford’s 2024 AI Index Report, it indicates two challenges that we currently face in today’s climate: a lack of reporting standardization for AI and the increasing number of regulations for AI within the United States.

The lack of reporting standardization falls under the differing framework for evaluation in AI. This is due to how rapidly artificial intelligence develops within recent years and the differing application approaches that came from it. In fact, within the same report, it states numerous top leading developers like Google and OpenAI test their models against different responsible AI benchmarks. The lack of systematic approach now makes it hard to compare the differing models, as they operate under different standards.

Meanwhile, the way AI operates is why there has been increasing regulation for AI in the United States. The Standford report states that within 2023 there has been increase of 25 new regulations compared to only 1 in 2016. As AI uses enormous amounts of big data to operate their tasks, some of the data can either be private or sensitive. A prime example of this is the latest trend, Generative AI.

ChatGPT has gotten over millions of users since its release back in 2022. With users inputting data, ChatGPT has quickly refined its responses through the ingestion of the substantial amount of data added into its database. Employees, who can see the potential increase in productivity, may input enterprise data to leverage the tool for their own benefits. This data, potentially inside business secrets or copyrighted material, is now out to the public with a substantial risk of data violations.

With that, the next post will go more in-depth on the application of what is possible through AI.

Sources:
AI Index Report 2024 – Artificial Intelligence Index (stanford.edu)
The most important AI trends in 2024 – IBM Blog

The post Exploring the Evolving Landscape of Artificial Intelligence in 2024 appeared first on Valore Partners.

The journey of AI is a fascinating tale of human ingenuity, marked by groundbreaking research, technological advancements, and profound societal impacts. Join us as we briefly explore the evolution of AI, uncovering some key developments and challenges that have shaped this dynamic field over the decades.

The 1950’s and Alan Turning

Many consider the beginnings of AI to date from the mid-20th century and Alan Turing, a visionary who provided groundbreaking thought relative to artificial intelligence.  While playing an indispensable role in the advancement of modern computers, he also proposed a method to determine if a machine’s intelligence could be indistinguishable from a human.  This came to be known as the Turing Test. Despite only being a theoretical framework, the question on whether machines could think help conceptualize the idea of artificial intelligence before the term was coined.

1956: Dartmouth Summer Research Project on Artificial Intelligence

Soon after Alan Turing proposed his framework, John McCarthy and Marvin Minskey held the famous Dartmouth Conference in the summer of 1956. The proposal for this conference was a discussion about thinking machines and is now considered as the founding event of artificial intelligence as a field of study. Here McCarthy invited many brilliant minds such as Allen Newell, Cliff Shaw, and Herbert Simons who programmed the Logic Theorist.

The Logic Theorist was the first computer software engineered to perform automated reasoning, which was then dubbed as the first artificial intelligence program. This was the turning point for artificial intelligence as it changed from an idea into a feasible reality. The Dartmouth Conference is considered a seminal event in the history of AI as it sets the stage for future research and development in areas like machine learning and natural language processing.

1960’s – 1970’s: Formative Years

In the wake of the Dartmouth Conference, there was a surge of interest and research in artificial intelligence. A notable example is the establishment of AI studies at the Massachusetts Institute of Technology (MIT) by McCarthy and Minsky.  This lab became a hotbed of innovation and development in the field of artificial intelligence.

One early demonstration of such applications was by Joseph Weizenbaum, who created ELIZA in 1966. ELIZA, an early natural language processing program and one of the first chatbots, paved the way for applications like Siri and Alexa. Another significant development was the creation of the programming language LISP by McCarthy, officially released in 1960. LISP became a favored language for programming artificial intelligence.

Research was not limited to MIT as other important institutions, such as Stanford, Carnegie Mellon, and Edinburgh, were also making significant advancements in AI research. The advocacy and research efforts from these institutions incentivized the Defense Advanced Research Projects Agency (DARPA), a US government agency, to fund AI research, mirroring similar efforts by nations worldwide.

1970s – 1990s: AI winter

As government agencies’ patience drew thin, especially by DARPA with the slow development of the Speech Understanding Program, funding began to dry up.

As usual, lack of funding leads to lack of interest, and overall progress in artificial intelligence research slowed to a crawl. During these times, public sentiment towards AI was marked by skepticism and disappointment. A pivotal moment leading to the decline in AI enthusiasm was the publication of the 1973 Lighthill Report in the UK. This report, authored by Sir James Lighthill, criticized the progress of AI research, arguing that the achievements of AI were neither practical nor applicable as working systems.

Though the 1980’s encountered an AI revival driven by the development of expert systems, this resurgence quickly faded as these systems proved to be weak and expensive. Many of the problems came from the current computers of the time. Many researchers blame the lack of computer memory and processing power holding back applications from doing anything truly useful.

2000 – Present: Revival of AI

All progress did not stop, however. In 1985 Carnegie Mellon University began working on an expert system called ChipTest to play chess.  It then moved to IBM where it was renamed Deep Thought and then Deep Blue.  In 1996 Deep Blue lost to world champion Garry Kasparov 4 games to 2, but in a 1997 rematch, Deep Blue beat Kasparov winning 2 games and drawing 3.  This event became a pivotal moment in the advancement of computer processing, and the potential for artificial intelligence to surpass human intelligence.

AI was now headed to people’s homes.  A spin-out from the Stanford Research Institute’s Artificial Intelligence Center, Apple released Siri in 2010. A virtual assistant that can assist in everyday tasks, showcased the advancement of natural language processing and machine learning.  Alexa and others followed.

A step further came in 2022 with the release of ChatGPT, a chat bot built on GPT-3.5 large language model, emulating human conversations through text. Generative AI has now taken off as a field to popularize artificial intelligence capabilities through the use of deep learning and general access to big data,

These events renewed interest in artificial intelligence, leading to its current revival.  With advancing computing capabilities and the capturing of, and access to, incredible amounts of data, artificial intelligence has rapidly evolved to become a part of everyday life.

Seeing how far the field has come during its brief history, our next post will take a look at the current state of artificial intelligence.

The post A Brief History of Artificial Intelligence appeared first on Valore Partners.

In this series of articles, we will provide a snapshot of the world of Artificial Intelligence (AI), from its terminology and origins to future trend predictions to help with understanding how this modern technology came to dominate the world’s conversation about our collective future.

Machine learning, deep learning, neural networks, supervised networks and more. These are some of the terms bandied about in articles and podcasts about AI.  We thought we’d start here – what do these terms mean in the context of a discussion about AI?

Let’s begin by understanding what Artificial Intelligence is at its core. IBM says, “Artificial intelligence, is technology that enables computers and machines to simulate human intelligence and problem-solving capabilities.”  When used independently or in conjunction with other technologies like sensors, geolocation, or robotics, AI can undertake tasks that would typically necessitate human intelligence or involvement. Now, let’s take it a step further and define some of the basic terms and concepts.

Machine Learning

The folks at Google Cloud like to say Machine Learning automatically enables a computer or system to learn and improve from experience rather than via explicit programming. Machine learning uses algorithms to analyze large amounts of data, acquire insights, learn from them and make informed decisions.  Machine learning is not about teaching a computer to mimic human intelligence, it is specifically aimed at teaching a computer to perform specific tasks on its own.

Deep Learning

Deep Learning is a subset of machine learning. Unlike machine learning, deep learning removes the pre-processing of data and relies on patterns found within. This allows the ingesting of big unstructured data such as an image or a sentence without human intervention. This allows a computer to perform more complex tasks such as driving vehicles and picking the fastest route on the fly as traffic conditions change.

Neural Networks

Neural Networks are another important subset of machine learning and the backbone to deep learning. Inspired by how the human brain learns; Amazon likes to describe Neural Networks using interconnected nodes in a layered structure that signal each other as they pass data. This creates a system that is adaptive, creating an ability to learn from mistakes.  Thus, these artificial neural networks can attempt to solve complicated problems like summarizing documents or recognizing faces.

Natural Language Processing

Natural Language Processing (NLP) assists computer systems with understanding and interpreting human language. Using deep learning algorithms, Natural Language Processing is used in tons of daily activities – chatbots, speech recognition, and machine translation.

Robotics

Robotics systems are a form of artificial intelligence that can replicate or substitute human action of controlling physical objects. Applications for this subset can be found in the manufacturing industry as they automate the manufacturing processes.

Expert Systems

Expert system (knowledge-based systems) is a subset of artificial intelligence that stimulates the decision-making capabilities of a human expert within specific domains. This is done by analyzing a knowledge repository made by domain experts, with formats varying from databases to semantic network. An inference engine is built which deduces information from the rules and data provided through the knowledge repository, providing recommendations like an expert in the field.

Computer Vision

Computer Vision is a subset of artificial intelligence that teaches computer systems to process information from visual inputs such as digital images or videos. The computer systems are then able to make predictions and identify defects or issues.

In part 2 of our series, we’ll provide a brief history and evolution of Artificial Intelligence.

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