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Bringing Machine Learning Projects to Reality from Concept to Finish



Bringing Machine Learning Projects to Reality from Concept to Finish

The greatest innovation ever made by humanity is stalling out of the gate. Projects using machine learning have the potential to assist us in navigating the biggest hazards we face, such as child abuse, pandemics, wildfires, and climate change. It can improve healthcare, increase sales, reduce expenses, stop fraud, and streamline manufacturing.

However, ML projects frequently fall short of expectations or fail to launch at all. They incur heavy losses when they stall before deploying. The fact that businesses frequently concentrate more on the technology than on the best way to use it is one of the main problems. This is akin to being more enthusiastic about a rocket’s development than its eventual launch.

Changing a Misplaced Focus to Deployment from Technology

The issue with ML is its widespread use. Despite all the excitement surrounding the underlying technology, the specifics of how its implementation enhances corporate operations are sometimes overlooked. ML is currently too hot for its own benefit in this sense. The lesson has finally dawned on me after decades of consulting and organizing ML conferences.

Today’s ML enthusiasm is overblown because it perpetuates the ML fallacy, a widespread misunderstanding. It operates as follows: ML algorithms’ models are intrinsically valuable (which is not always true), as they can successfully produce models that stand up for new, unforeseen scenarios (which is both amazing and true). Only when machine learning (ML) generates organizational change, or when a model produced by ML is used to actively enhance operations, does ML become valuable. A model has no real value until it is actively employed to change the way your company operates. A model won’t deploy itself and won’t resolve any business issues on its own. Only if you use ML to cause disruptions will it truly be the disruptive technology that it promises to be.

Regrettably, companies frequently fall short in bridging the “culture gap” between data scientists and business stakeholders, which keeps models hoarded and prevents deployment. When it comes to “mundane” managerial tasks, data scientists—who carry out the model creation step—generally don’t want to be bothered with them and become completely fixated on data science. They frequently overlook a strict business procedure that would involve stakeholders in cooperatively planning the model’s adoption and instead take it for granted.

However, a lot of business people, particularly those who are already inclined to disregard the specifics because they are “too technical,” have been persuaded to believe that this amazing technology is a magic bullet that will fix all of their problems. When it comes to project specifics, they defer to data scientists. It’s difficult to convince them, though, when they eventually have to deal with the operational disruption that a deployed model would cause. The stakeholder is caught off guard and hesitates before changing operations that are essential to the business’s profitability.

The hose and the faucet don’t connect because no one takes proactive responsibility. The operational team drops the ball far too frequently when the data scientist presents a workable model and they aren’t prepared for it. Although there are amazing exceptions and spectacular achievements, the generally dismal performance of ML that we currently see portends widespread disillusionment and possibly even the dreaded AI winter.

The Resolution: Business Machine Learning

The solution is to meticulously plan for deployment right from the start of every machine learning project. It takes more preaching, mingling, cross-disciplinary cooperation, and change-management panache to lay the foundation for the operational change that deployment would bring about than many, including myself, first thought.

In order to do this, a skilled team needs to work together to follow an end-to-end procedure that starts with deployment backward planning. The six steps that make up this technique, which refer to as bizML, are as follows.

Determine the deployment’s objective

Describe the business value proposition (i.e., operationalization or implementation) and how machine learning (ML) will impact operations to make them better.

Example: In order to prepare a more effective delivery process, UPS makes predictions about which destination addresses will receive package deliveries.

Decide on the prediction’s objective

Describe the predictions made by the ML model for each unique case. When it comes to business, every little detail counts.

Example: How many shipments across how many stops will be needed tomorrow for each destination? For instance, by 8:30 a.m., a collection of three office buildings at 123 Main St. with 24 business suites will need two stops, each with three packages.

Decide on the metrics for the evaluation

Establish the important benchmarks to monitor during the deployment and training of the model, as well as the performance threshold that needs to be met for the project to be deemed successful.

Examples include miles traveled, gasoline gallons used, carbon emissions in tons, and stops per mile (the more stops per mile a route has, the more value is gained from each mile of driving).

Get the information ready

Establish the format and format requirements for the training data.

Example: Gather a plethora of both positive and bad instances so that you can learn from them. Include places that did receive delivery on particular days as well as those who did not.

Get the model trained

Utilize the data to create a prediction model. The object that has been “learned” is the model.

Neural networks, decision trees, logistic regression, and ensemble models are a few examples.

Put the model to use

Apply the knowledge gained to new cases by using the model to provide predicted scores, or probabilities, and then take appropriate action based on those scores to enhance business operations.

Example: UPS enhanced its system for allocating packages to delivery trucks at shipping centers by taking into account both known and anticipated packages. An estimated 18.5 million miles, $35 million, 800,000 gallons of fuel, and 18,500 metric tons of emissions are saved annually because to this technology.

These six phases outline a business procedure that provides a clever route for ML implementation. Regardless of whether they work in a technical or business capacity, everyone who wants to engage in machine learning projects needs to be knowledgeable about them.

Step 6 culminates in deployment, and then you’re done. Now to start something new. BizML just marks the start of a continuous process, a new stage in managing enhanced operations and maintaining functionality. A model needs to be maintained when it is launched, which includes regular monitoring and refreshing.

Completing these six stages in this order is practically a given. Let’s begin at the conclusion to comprehend why. Model training and deployment are the two primary ML processes, and they are the last two culminating steps, steps 5 and 6. BizML drives the project to its successful conclusion.

Step 4: Prepare the data is a known prerequisite that comes right before those two and is always completed before model training. For machine learning software to function, the data you feed it must be in the correct format. Since corporations began using linear regression in the 1960s, that stage has been a crucial component of modeling initiatives.

You have to do commercial magic first, then the technical magic. That is the purpose of the first three steps. They initiate a crucial “preproduction” stage of pitching, mingling, and working together to reach a consensus on how machine learning will be implemented and how its effectiveness would be assessed. Crucially, these preliminary actions encompass much more than just deciding on the project’s economic goal. They push data scientists to step outside of their comfort zone and collaborate closely with business-side staff, and they ask business people to delve into the specifics of how forecasts will change operations.

Including Business Partners in the Process

While not frequent, following all six of the bizML practice’s steps is not unheard of. Even though they are rare, many machine learning programs are quite successful. Though it has taken some time for a well-known, established framework to emerge, many seasoned data scientists are familiar with the concepts at the core of the bizML framework.

Business executives and other stakeholders are the ones who probably need it the most, but they are also the ones who are least likely to know about it. As a matter of fact, the general business community is still unaware of the necessity of specialist business practices in the first place. This makes sense because the popular story misleads them. AI is frequently overhyped as a mysterious yet fascinating panacea. In the meantime, a lot of data scientists would much rather crunch figures than take the time to explain.


AI’s Revolutionary Effects on Startups’ Patent Analysis




AI's Revolutionary Effects on Startups' Patent Analysis

Every startup is eager to introduce the world to its next big idea. Additionally, it’s always a good idea to file for a patent in order to protect their concept from being copied or corrupted. Since patents are by definition unique, entrepreneurs must be absolutely convinced that their concept is unique before applying for one. This can be achieved by performing an exhaustive patent search beforehand. Since speed and accuracy are critical, AI has a strong case to accelerate the patent search process for startups.

The Value of Searching for and Analyzing Patents

  • One useful method for learning about market trends is to study the patent applications that are currently pending. If a single product category has several patents, it may be a competitive market meeting different consumer requirements in that industry. Finding potential technological gaps and uncharted territory is another benefit of conducting a search and analysis of patents. For example, the firm may discover that while their initial concept has been investigated, there is a chance to safeguard and market a complementary product that would appeal to the same customer base.
  • Startups can save time and money by performing a preliminary patent search to make sure that (a) their idea is not already patented or a commercial product, or (b) it does not fall under a class of products that is not eligible for patent protection. They will save the expenses and future legal headaches of having to deal with patent infringement because of this.

Recognizing The Difficulties in Doing A Patent Search

  • In every industry, searching for patents is an extremely intricate and time-consuming procedure. It is necessary to create intricate Boolean searches, sift through all of the available patent data, and identify the key elements—such as highlighting murky areas that should be sent to a lawyer for advice. In terms of taking advantage of current market opportunities, this can be counterproductive because it can take a long time.
  • It is impossible to overestimate the importance of precision in patent searches and the analysis that follows. Any inaccuracy could result in the rejection of the patent, lawsuits alleging patent infringement, and a substantial loss of time and money. assessing whether the proposed invention is simply a copy of an existing filing made by someone else, or a version with discernible variations, becomes more challenging when assessing the criteria for patent duplication.

Artificial Intelligence in Patent Analysis and Search

  • Artificial intelligence is considerably faster than humans at finding and analyzing any type of data. Previous natural language search engines were unable to decipher the meaning and intention contained in the user-provided innovation description. However, AI-driven patent search has matured and can now produce significantly improved search relevance and extremely accurate results thanks to pre-trained Large Language Models. As a result, it is the perfect solution for tasks that require a lot of time, including patent search and analysis. Startups are already moving more quickly and closer to their eventual patent registration with the help of a number of AI search and result analysis tools.
  • Search efficiency: The efficiency that AI offers to the search process is its most evident benefit. The user may “rely” on the AI search system to provide extremely accurate results in a matter of seconds, saving them the trouble of manually crafting complex search terms to comb through patent data.
  • Semantic assessments of patents can be performed by AI trained in natural language processing (NLP). This helps them to appropriately read any sections with ambiguous wording and make sense of regional variations in language. This is especially helpful when examining patent claims for various iterations of the same invention.
  • Classification algorithms: Not all patent-related information is probably arranged according to how the startup in question views the technology. The end user can be presented with a rated and classed result by training machine learning algorithms to sort the data based on relevance.
  • Visualization tools: AI can classify and highlight important information in an understandable visual report by organizing and summarizing the data. Making educated decisions and presenting findings to pertinent parties would be made simpler as a result.

AI’s Future Directions for Searching and Analyzing Patents

Artificial intelligence has many uses in the analysis and search of patents. In order to establish a single, transparent chain of information, the integration of AI with blockchain and IoT is now being investigated. Even while some of these AI apps can be pricey, new choices are being created daily, so in the end, costs will be reduced for startups with tight budgets. AI algorithms are just getting started, but they have the potential to speed up the patent registration process enormously, so firms who use them now will be the first to see their innovative ideas come to fruition.

PatSeer is an AI-based patent search engine that leads the way in innovation by allowing users to navigate the IP landscape with never-before-seen ease thanks to its rich Boolean and AI search functionalities. With the platform’s easy-to-use interface, startups can do thorough patent searches to make sure their ideas are original and eligible for patent protection.

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An Innovative Text-to-Video AI Startup Hopes to Revolutionize New York Filmmaking




An Innovative Text-to-Video AI Startup Hopes to Revolutionize New York Filmmaking

With its innovative text-to-video generator, a New York-based firm is creating waves and has the potential to completely change the entertainment and filmmaking industries in an era where creativity and technology are interacting more than ever. Though it is still in the early phases of development, this cutting-edge tool has the potential to usher in a new era of content production by enabling users to turn textual storylines into full-length movies. In addition to its potential to democratize filmmaking, the startup’s ambitious project, which makes use of cutting-edge artificial intelligence (AI), is gaining attention for its ramifications for the entertainment industry as a whole.

A Peering Into the Future of Cinema

An AI-powered platform that translates textual input to produce related visual information is at the center of this innovative project. By transforming photos into dynamic worlds, Google’s Genie is one AI model that has already started to revolutionize interactive storytelling. This technology builds on the foundation that these models have created. By making it possible to create intricate, narratively rich video content from straightforward written descriptions, the text-to-video generator seeks to go beyond this and may pave the way for a new generation of filmmakers and content producers.

Innovations in Technology and Creative Liberty

The startup’s technology uses artificial intelligence (AI) to study and comprehend character development, narrative structures, and visual storytelling methods. By doing this, it can create videos that effectively visually convey a story in addition to telling it. In order to comprehend the nuances of human creativity, complex AI algorithms and machine learning approaches have been devised and polished. Wide-ranging ramifications result from this technology, which gives people who might not have the means or technical know-how normally needed for film production previously unheard-of creative freedoms.

Difficulties and Ethical Issues

Despite the enthusiasm surrounding this technological innovation, there are many obstacles in the way of bringing the idea to fruition. Discussions are centered on ethical issues, including copyright concerns, the veracity of AI-generated content, and the effect on conventional filmmaking roles. Furthermore, it will be crucial to address these issues in a way that respects the rights of all parties involved as well as the creative process as this technology develops. The firm is dedicated to overcoming these obstacles in order to provide a framework that guarantees the ethical and responsible application of AI in the creative industries.

Unquestionably, the New York startup’s text-to-video converter has the potential to revolutionize entertainment and democratize film production as it continues to evolve. This invention has the potential to upend storytelling conventions and empower budding filmmakers alike. Such technology has an impact on marketing, virtual reality experiences, and instructional content in addition to the entertainment sector. The nexus between artificial intelligence (AI) and human creativity promises to open up new vistas and redefine storytelling as we approach this revolutionary period.

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Vietnam’s Skills In AI Help Precision Medicine Companies




Vietnam's Skills In AI Help Precision Medicine Companies

Investments in health technology, particularly in precision medicine, are benefiting from Vietnam’s quick advances in artificial intelligence and technology.

According to DealStreetAsia’s Data Vantage’s “SE Asia Deal Review: Q4 2023” report, health tech investments in Southeast Asia remained up despite the general pessimism surrounding fundraising in 2023. The sector’s startups raised $580 million from 60 agreements. Vietnam was in third place in the area with 3.9% of the investments, while firms in far larger economies like Singapore and Indonesia took home the majority of the funding for health tech.

According to analysts, there has been a surge in interest in Vietnam’s health tech sector in recent times, as there is optimism that the utilization of artificial intelligence can accelerate advancements like precision medicine.

“Vietnam has numerous promising companies in this sector, the market is still at an early stage,” said Vy Le, co-founder and general partner of the venture capital firm Do Ventures.

Precision medicine, also referred to as “personalized medicine,” creates individualized treatments for conditions like cancer, diabetes, or Alzheimer’s by using a patient’s genetic profile. Put another way, a personalized strategy based on the patient’s DNA replaces the typical one-size-fits-all approach to diagnosis and therapy. The promise of AI in this area is that people will be able to affordably sequence their genomes.

Gene Solutions is one of the precision medicine firms in Vietnam that has drawn venture capital. In its Series B funding round, the business brought in $21 million last year. According to the Data Vantage analysis, the transaction, which was led by Mekong Capital, ranked as the seventh-largest health tech deal in Southeast Asia in 2023. Mekong Capital made a $15 million investment in Gene Solutions in 2021.

DealStreetAsia revealed in September 2023 that Gene Solutions is aiming to raise $50 million in a Series C investment.

Established in 2017, Gene Solutions focuses on using DNA markers to identify the existence of specific diseases. It has aided in the detection of chromosomal abnormalities in expectant mothers, averting genetic issues, and assisting with in-vitro fertilization. It seeks to lower the cost of genetic testing and increase accessibility.

One of Gene Solutions’ competitive advantages, according to Chris Freund, founder and partner of Mekong Capital, is “how fast-moving” company. For instance, when we first invested, it was just an idea to grow outside of Vietnam. However, in the last two years, they have successfully partnered with top hospital groups and cancer institutes in [the] Philippines, Malaysia, Indonesia, Thailand, and Singapore, with partial support from a Singaporean lab.

Gene Solutions has completed more than 350,000 genetic tests in the previous five years.

GeneStory is another company in the field; Vingroup founded it in 2022 with a charter capital of 102.3 billion dong ($4.4 million). GeneStory seeks to offer “fast and comprehensive genetic testing services based on a large Vietnamese dataset, exclusively for Vietnamese people.” But in 2022, the conglomerate itself sold a confidential interest in GeneStory. In order to develop individualized health care programs, the startup provides assessments of people’s medical, physical, and dietary risks as well as hereditary characteristics.

Vietnamese venture-backed precision medicine businesses also include Genetica Company, which uses artificial intelligence (AI) to decipher DNA. The 2018-founded company received $2.5 million from Silicon Valley investors in a pre-Series A investment round in 2021.

Genetica has introduced a gene-decoding device that employs artificial intelligence (AI) to determine a person’s genetic susceptibility to respiratory virus infection.

Southeast Asia is seeing a boom in genomic research and development at the same time as interest in precision medicine. The “Harnessing Genomic Medicine and Gene NFT in Southeast Asia” report by DealStreetAsia and Genetica, published in August 2023, states that the region’s unique and diverse genetic makeup is being highlighted through the development of genomic datasets driven by both private-sector initiatives and government-supported programs.

AI has been used in healthcare for a longer period of time than in many other industries, according to Yinglan Tan, CEO and founding managing partner of Insignia Ventures Partners. Applications of AI in healthcare include risk assessment, predictive analytics, and medical imaging. He emphasized that the Asia-Pacific area, particularly Southeast Asia, presents substantial growth potential, holding a 13% share of the worldwide AI health care market.

The increasing need for individualized health care solutions is one of the main factors driving funding for precision medicine firms. Customers are looking for specialized medical solutions as they grow more health-conscious.

“As the tests become even more precise over the coming years, it will enable Gene Solutions to detect diseases with increasingly smaller DNA segments. The cost of those tests will also come down. Eventually, such tests will be affordable for the mass market in Vietnam and Southeast Asia,” said Freund of Mekong Capital.

Through a number of programs and incentives, the Vietnamese government has also been instrumental in supporting the development of precision medicine firms. With the help of the government, a favorable atmosphere for entrepreneurs has been established, drawing both domestic and foreign investors to the emerging health technology market.

Investors are conscious of the constraints, too, such as the fact that the regulatory environment for health IT businesses is still developing. “Investing in biotech companies is typically challenging for VC funds in Vietnam. This industry demands specialized funds with experts in the field,”, according to Vy Le of Do Ventures.

In addition, venture capital funds usually have an investment horizon of four to five years, but the biotech sector needs more time to succeed. This implies that additional government funding is needed. Le gave the example of South Korea, where the government runs a fund specifically intended to invest in biotech investments at different phases of development.

However, new trends in fundraising give the industry hope.

The “The State of Healthtech in SE Asia 2023” DealStreetAsia Data Vantage report discovered that from January 2020 to September 2023, 46% of the region’s health tech startups’ total deal volume and 72% of their equity funding came from investments in deep tech fields related to health care, such as genomics, molecular biology, artificial intelligence, and biometric sensing.

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