Top 10 Open Source Artificial Intelligence Software in 2021
Artificial Intelligence And Human Intelligence: Competition Or Compatibility?
"Artificial Intelligence could greatly improve efficiency and decrease cost: generating and analysing housing or employment projections, reviewing and categorising site submissions, managing consultation and even auto-generating reports and analyses"- John Mason - Carter Jonas
Imagine a future where masterplans and house type packs can be reviewed and tweaked at the click of a button. Where you can talk to a chatbot about whether your fence requires planning permission. Where consultation on local plans or individual planning applications is targeted, tailored to your interests, and timely. Where local authority processes are streamlined and efficient, decisions are made on time, and officers have plenty of availability to discuss your proposals.
Sounds too good to be true? Perhaps it is.
Over 3.5 million planning applications are submitted every year in the UK. Over one-third of planning applications contain basic errors, and 250,000 hours are spent by council officers running validation checks.
The Alan Turing Institute has already run pilots on how application validation could be automated through the use of machine learning, training computers to "read" plans and documentation to check for errors.
It found great potential to automate parts of the process, but the systems they trained were unable to differentiate between (for instance) existing and proposed plans, something a human could tell at a glance.
It is almost cliché to bemoan the length of time it takes to produce a new Local Plan – up to five years in some cases, by which point they could be hopelessly out of date. From the effects of the pandemic to the adoption of new technology to addressing the climate crisis, plan makers must be able to adapt quickly to fast-moving changes, but are currently unable to do so due to labyrinthine bureaucracy and the sheer amount of evidence that must be processed.
Artificial Intelligence could greatly improve efficiency and decrease cost: generating and analysing housing or employment projections, reviewing and categorising site submissions, managing consultation and even auto-generating reports and analyses.
The DLHUC's PropTech engagement fund is being used by 13 local authorities across the country to pilot the use of AI to manage public consultation on Local Plans. Authorities have adopted technology in a variety of ways: for instance, Greater Cambridge analysed social media feedback that wasn't being captured on the consultation portal, whilst Southampton used 3D models to show how new proposals would look.
AI is able to review consultation responses and automatically categorise them, pick out key themes and identify trends. This promises significant improvements in the ability to run Local Plan consultations, which can attract tens of thousands of comments which currently take an enormous amount of time to process.
At the other end of the scale, could Artificial Intelligence be used to review minor planning applications?
Householder applications, Certificates of Lawfulness or conditions discharge take a significant amount of officer time to process but are for the most part relatively simple, requiring objective decisions on whether the submission accords with specific legislation.
As with the validation process, this could in theory be done by a computer program, with a planning professional required to review the final recommendation. Similarly, simple pre-application enquiries for small-scale development could be automated with a chatbot: you could interact with your planning department in the same way you would with your bank or mobile phone provider.
Are we then heading to a future where a development proposal can be managed, submitted, and determined by a computer?
I don't think so. Artificial Intelligence has no intrinsic agency (it must be told what to do) and no accountability (its output must be evaluated by an accountable human). Have the consultation responses been summarised correctly? Do the auto-generated parts of a report make sense? Where have the data used in models or reports come from? Are there inaccuracies? Is it replicating unintended biases?
Planning in the UK is not a "tick-box" exercise and I do not believe should it be. Planning relies on the exercise of judgement and the weighing up of the planning balance. Considerations of design or the impact of a proposal on heritage assets are subjective.
Applicants and officers need room for discussion on where trade-offs or improvements can be made, and where departures from planning policies can be justified. And of course, decisions must have some kind of democratic oversight to ensure public good is balanced against private interest.
And we must be mindful of potential downsides. Automation could enable the targeting of more specific groups on a Local Plan or application consultation, aiding public engagement. On the other hand, the tailoring of consultation to what a computer perceives your interests to be could lead to an artificial narrowing of options or reinforcement of filter bubbles.
And whilst developers working across different authorities could benefit from the standardisation of validation requirements, automation could do away with the creativity and "colour" of individually prepared Local Plans or officer reports.
Standardisation of masterplans to ensure they can be read by computers could also lead to further homogenisation of new developments at the expense of innovation and the ability to tailor a layout to site-specific circumstances.
Artificial Intelligence is coming whether we like it or not. Local authorities, companies and individuals need to be able to be able to adopt, use and understand these tools, requiring time and investment. It's no secret that many planning departments and consultancies are struggling with resourcing at the moment, which could lead to a future of winners and losers.
Nevertheless, Artificial Intelligence has enormous potential to speed up the development process for applicants and authorities. AI will transform the way we undertake data-driven and administrative tasks, easing workloads and allowing us to spend more time "planning".
Negotiating good planning outcomes will continue to require human actors to exercise nuance, common sense, creativity and critical judgement, all things that cannot – and should not – be automated.
Master's (MS) In Machine Learning And Artificial Intelligence
Who is the Master's in Artificial Intelligence and Machine Learning program for?Drexel's College of Computing & Informatics' Master of Science in Artificial Intelligence and Machine Learning (MSAIML) provides a strong foundation in the artificial intelligence (AI) and machine learning fields with foci on mathematical foundations, algorithms, tools and applications as they pertain to artificial intelligence and machine learning. Students will pursue an applied or computational track and will gain competency in fundamental methods and techniques in artificial intelligence and machine learning. Their foundational understanding will be applied to real data sets and data analysis tasks with the help of state-of-the-art technologies, tools, and platforms. The Master of Science in Artificial Intelligence and Machine Learning program culminates with a two-term capstone experience where students work on a real world or research problem using the knowledge they have gained throughout the program.
Note that this degree has two concentrations available: computational and applied.
Fast FactsHours
Full-time (FT) or Part-time (PT)
Time-to-Degree
2-3 years (FT); 2-4 years (PT)
Format
On campus or online
Term Starts
Fall, Winter
Curriculum Design
Traditional Course-by Course or Modular Certificate.
Hands-on experience?
Hands-on, collaborative capstone project; Graduate co-op is available for on-campus, full-time students.
Technical experience required?
Yes – a bachelor's degree in computer science or a STEM-related discipline is required for the computational concentration. Students without a technical background who wish to pursue the computational track will likely need to complete the Graduate Certificate in Computer Science Foundations before beginning this master's degree.
GRE required?
Recommended for students with a GPA below 3.0 on the 4.0 U.S. GPA scale, or equivalent
Meets F1 Visa STEM Requirements?
Yes
Artificial Intelligence and Machine Learning Program OverviewThis graduate program explores the discipline's fundamental mathematics, developing related tools, and applying AI and ML to various real-world problems.
Coursework covers a broad, interdisciplinary range of topics, including data science, both theoretical and applied artificial intelligence and machine learning, mathematics and algorithms for artificial intelligence and machine learning, and domain-specific applications. The program culminates in a collaborative, hands-on capstone project.
Courses are taught by CCI's world-class faculty who have active research experience in machine learning, computer vision, game AI, data science, cognitive science, high-performance computing, software engineering, and applied machine learning in gaming and in security.
Request More InformationBe in touch with us to get answers to all your questions! We can connect you with a Recruitment Specialist, one of our Graduate Dean's Ambassadors, or a faculty member to help. Contact our graduate recruitment team at cciinfo@drexel.Edu and we'll get back to you soon.
MS in AI & Machine Learning CurriculumIMPORTANT NOTE: Drexel operates on the quarter, not semester, system, offering classes during four 10-week terms throughout the year. (You are not required to take classes every quarter, but full-time students must complete the degree in 3 years.)
Our master's degrees are 45 credits (15 courses) which is equivalent to 30-33-credit degrees at other universities. The benefit of the quarter system is that students receive instruction in more topic areas than through the semester system and have more freedom to personalize their curriculum using electives.
This degree is available through a traditional curriculum or a modular certificate curriculum.
Traditional CurriculumCCI's Master's Degree in Artificial Intelligence and Machine Learning consists of:
Full-time, on-campus students who are accepted for fall and winter term admissions can also choose a Graduate Co-op option. Please visit the Steinbright Career Development Center to learn more about cooperative education and its benefits.
Please visit Drexel's Course Catalog for a full description of each required course and elective for this program. You can also find a sample Plan of Study for the degree.
Modular Certificate CurriculumThis curriculum allows the student to select several certificates in order to 1.) gain a foundation in AIML principles and practice and 2.) focus in-depth on specific skills and knowledge through certificates, customized to your needs and professional goals.
Following the modular certificate option enables you to earn marketable credentials — skills set-based certificates — on your way to completing the master's degree, and it lets you complete a certificate, step away from the program if you need to, and return to seamlessly pick up where you left off.
In the modular certificate option, you will select:
In addition, you may need to take a small number of additional courses in order to complete the degree requirements.
There are a number of possible certificate combinations, and you will work with your adviser and the faculty to create the sequence that best fits your needs and background.
The following certificates can be used to create the MS in Artificial Intelligence and Machine Learning:
Applied Concentration
Core
Major Electives
Flexible Electives (optionally choose one)
Computational Concentration
Core
Major Electives
Flexible Electives (optionally choose one)
Full-time, on-campus students who are accepted for fall and winter term admissions can also choose a Graduate Co-op option. Please visit the Steinbright Career Development Center to learn more about cooperative education and its benefits.
Please visit our Graduate Admissions section for application deadlines.
Finances TuitionPlease visit Drexel's official Graduate Tuition & Fees webpage. Note that this master's degree requires 45 quarter credits.
ScholarshipsThere are a variety of sources of financial aid and scholarships to help you fund your master's degree — explore them here.
Get in TouchOur Recruitment Specialists are here to walk you through the sometimes confusing world of financing your graduate education. Get in touch with us now by filling out this form.
Artificial Intelligence (AI) In Computer Vision Market To Surpass USD 148.8 Billion By 2031
(MENAFN- EIN Presswire)
WESTFORD, MASSACHUSETTS, UNITED STATES, June 24, 2024 /EINPresswire / -- Artificial Intelligence (AI) in Computer Vision market size was valued at USD 20.7 billion in 2022 and is poised to grow from USD 25.8 billion in 2023 to USD 148.8 billion by 2031, growing at a CAGR of 24.5% during the forecast period (2024-2031).
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The expansion of AI in the computer vision market is continuing with its significant use across several fields such as manufacturing, utilities, energy and automobile industry. Artificial intelligence computer vision is a hot research issue. The significance of computer vision technology to real world applications, from e-commerce to the game industry, transportation, healthcare and daily activities cannot be underscored enough.
Driven by breakthroughs in deep learning, rising need for automation, and applications in industries like healthcare, automotive, and security, the global market for artificial intelligence in computer vision is expanding rapidly. The image recognition features are better, as well as connections to the internet of things (IoT) and there is more global funding and research work.
Visionary Innovation's AI Breakthroughs in Autonomous Vehicles, Healthcare, and Smart Cities in Near Future
The following are the key Artificial Intelligence (AI) in Computer Vision Trends that will shape the growth of the market in the next 5 years
.Google disclosed the new computer vision algorithm breakthrough during May 2024 that greatly enhances object detection accuracy, even in difficult conditions. The transition from traditional methods to more automated ones might happen quickly following the invention as far as adoption rates across different countries are concerned while also establishing fresh standards for driverless cars, security operations and medical care.
.Chinese tech company Huawei debuted its most recent AI-powered computer vision platform in April 2024, specifically designed for use in smart city applications. By employing advanced surveillance, traffic monitoring, and infrastructure maintenance, this invention is intended to outperform urban management. It is expected that the platform's implementation will do a lot to step up the smart city industry until 2029. Thus, market dynamics will never be the same while technology undergoes improvement.
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Long-Term Impact of NVIDIA's Industrial Vision and AWS's Agricultural Solutions Over the Decade
.In June 2024, NIVIDIA introduced an AI-powered vision platform for industrial automation and robotics. By enhancing precision and productivity, such an innovation is expected to revolutionize production processes entirely. In the coming decade higher output volumes and improved global competitiveness will probably feature as some of the long-term outcomes.
.In March 2024, AWS released an adaptable, Artificial Intelligence computer vision service targeting agriculture domain while improving crop surveillance system and yield predictions for sustainable agriculture support. It is expected to take up to 2034 for this system to have significant positive long-term impacts of increased agricultural output and enhancing worldwide food guarantee strategies.
View report summary and Table of Contents (TOC):
Pioneering AI Frontiers in Computer Vision Innovations
IBM unveiled a state-of-the-art AI computer vision system for medical imaging in February 2024 with the goal of detecting and diagnosing diseases early on. In January 2024, Microsoft released a broad AI platform designed for retail analytics with an aim of enriching customer experiences and simplifying operations. On its side, Samsung made public an AI-based monitoring system for increased security through immediate threat identification in April 2024. It is expected that as a group, these advancements will strongly advance healthcare, retail trade and security industries, bringing about more efficiency, accuracy and client contentment. In the near-potential time, the combined results are forecasted to hasten the adoption and integration of AI, hence, establishing new industry benchmarks and nurturing global technical progress during the coming years.
AI-Powered Evolution Driving Global Market Expansion
Companies such as Google, Huawei, NVIDIA, AWS, IBM, Microsoft and Samsung are developing their AI powered computer vision technologies at a rapid pace. These technologies will change industries like healthcare, retail, agriculture, manufacturing and smart cities. They enhance productivity levels, accuracy and efficiency besides paving way for radical impacts on environment consciousness and global competition. Over the next ten years, the market is expected to grow significantly with more investment and research, technological advancement and establishing new industry standards. This combined force will quicken the adoption of AI, resulting in substantial advantages for a variety of applications and enhancing people's quality of life everywhere.
Related Report:Artificial Intelligence Market
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