Wayne Blodwell, founder and chief exec of The Programmatic Advisory & The Programmatic University, battles through the buzzwords to explain why custom machine learning can help you unlock differentiation and regain a competitive edge.

Back in the day, simply having programmatic on plan was enough to give you a competitive advantage and no one asked any questions. But as programmatic has grown, and matured (84.5% of US digital display spend is due to be bought programmatically in 2020, the UK is on track for 92.5%), whats next to gain advantage in an increasingly competitive landscape?

Machine Learning

[noun]

The use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data.

(Oxford Dictionary, 2020)

Youve probably head of machine learning as it exists in many Demand Side Platforms in the form of automated bidding. Automated bidding functionality does not require a manual CPM bid input nor any further bid adjustments instead, bids are automated and adjusted based on machine learning. Automated bids work from goal inputs, eg achieve a CPA of x or simply maximise conversions, and these inputs steer the machine learning to prioritise certain needs within the campaign. This tool is immensely helpful in taking the guesswork out of bids and the need for continual bid intervention.

These are what would be considered off-the-shelf algorithms, as all buyers within the DSP have access to the same tool. There is a heavy reliance on this automation for buying, with many even forgoing traditional optimisations for fear of disrupting the learnings and holding it back but how do we know this approach is truly maximising our results?

Well, we dont. What we do know is that this machine learning will be reasonably generic to suit the broad range of buyers that are activating in the platforms. And more often than not, the functionality is limited to a single success metric, provided with little context, which can isolate campaign KPIs away from their true overarching business objectives.

Custom machine learning

Instead of using out of the box solutions, possibly the same as your direct competitors, custom machine learning is the next logical step to unlock differentiation and regain an edge. Custom machine learning is simply machine learning that is tailored towards specific needs and events.

Off-the-self algorithms are owned by the DSPs; however, custom machine learning is owned by the buyer. The opportunity for application is growing, with leading DSPs opening their APIs and consoles to allow for custom logic to be built on top of existing infrastructure. Third party machine learning partners are also available, such as Scibids, MIQ & 59A, which will develop custom logic and add a layer onto the DSPs to act as a virtual trader, building out granular strategies and approaches.

With this ownership and customisation, buyers can factor in custom metrics such as viewability measurement and feed in their first party data to align their buying and success metrics with specific business goals.

This level of automation not only provides a competitive edge in terms of correctly valuing inventory and prioritisation, but the transparency of the process allows trust to rightfully be placed with automation.

Custom considerations

For custom machine learning to be effective, there are a handful of fundamental requirements which will help determine whether this approach is relevant for your campaigns. Its important to have conversations surrounding minimum event thresholds and campaign size with providers, to understand how much value you stand to gain from this path.

Furthermore, a custom approach will not fix a poor campaign. Custom machine learning is intended to take a well-structured and well-managed campaign and maximise its potential. Data needs to be inline for it to be adequately ingested and for real insight and benefit to be gained. Custom machine learning cannot simply be left to fend for itself; it may lighten the regular day to day load of a trader, but it needs to be maintained and closely monitored for maximum impact.

While custom machine learning brings numerous benefits to the table transparency, flexibility, goal alignment its not without upkeep and workflow disruption. Levels of operational commitment may differ depending on the vendors selected to facilitate this customisation and their functionality, but generally buyers must be willing to adapt to maximise the potential that custom machine learning holds.

Find out more on machine learning in a session The Programmatic University are hosting alongside Scibids on The Future Of Campaign Optimisation on 17 September. Sign up here.

Visit link:
What is 'custom machine learning' and why is it important for programmatic optimisation? - The Drum

We often equate machine learning to fictional scenarios such as those presented in films including the Terminator franchise and 2001: A Space Odyssey. While these are all entertaining stories, the fact of the matter is that this type of artificial intelligence is not nearly as threatening. On the contrary, it has helped to dramatically enhance the overall user experience (UX) and to streamline many online functions (such as common search results) that we take for granted. Machine learning is also making its presence known within the digital gaming community. Without becoming overly technical, what transformations can we expect to witness and how will these impact the experience of the average gaming enthusiast?

Although games such as Pong and Super Mario Bros. were entertaining for their time, they were also quite predictable. This is why so many users have uploaded speed runs onto websites such as YouTube. However, what if a game actually learned from your previous actions? It is obvious that the platform itself would be much more challenging. This concept is now becoming a reality.

Machine learning can also apply to numerous scenarios. It may be used to provide a greater sense of realism with interacting with a role-playing game. It could be employed to offer speech recognition and to recognise voice commands. Machine learning may also be implemented to create more realistic non-playable characters (NPCs).

Whether referring to fast-paced MMORPGs to traditional forms of entertainment including slot games offered by websites such as scandicasino.vip, there is no doubt that machine learning will soon make its presence known.

We can clearly see that the technical benefits associated with machine learning will certainly be leveraged by game developers. However, it is just as important to mention that this very same technology will have a pronounced impact upon the players themselves. This is largely due to how games can be personalised based around the needs of the player.

We are not only referring to common options such as the ability to modify avatars and skins in this case. Instead, games are evolving to the point that they will base their recommendations off of the behaviours of the players themselves. For example, a plot may change as a result of how a player interacts with other characters. The difficulty of a specific level may be automatically adjusted in accordance with the skill of the player. As machine learning and AI both have the ability to model extremely complex systems, the sheer attention to graphical detail within the games (such as character features and backgrounds) will also become vastly enhanced.

We can see that the future of gaming looks extremely bright thanks to the presence of machine learning. While such systems might appear to have little impact upon traditional platforms such as solitaire, there is no doubt that they will still be felt across numerous other genres. So, get ready for a truly amazing experience in the months and years to come!

Excerpt from:
How Machine Learning is Set to Transform the Online Gaming Community - Techiexpert.com - TechiExpert.com

There is a need for more comprehensive prediction models for advanced age-related macular degeneration (AMD) that consider a wider range of risk factors. Researchers tested a prediction model and applied a machine learning algorithm that autonomously identified the most significant clinical, genetic, and lifestyle risk factors for AMD.

The training set, obtained from the Rotterdam Study I (RS-I), included 3,838 patients aged 55 years or older. Median follow-up was 10.8 years, and there were 108 incident cases of advanced AMD. The test set, obtained from the ALIENOR study, included 362 participants aged 73 years or older. Median follow-up was 6.5 years, and there were 33 incident cases of advanced AMD.

The following factors were retained by the prediction model:

In the RS-I group, the cross-validated area under the receiver operating characteristic curve (AUC) estimation was: at five years, 0.92; at 10 years, 0.92; and at 15 years, 0.91. In the ALIENOR cohort, at five years, the AUC was 0.92. The researchers noted that when it came to calibration, the prediction model underestimated the cumulative incidence of advanced AMD in high-risk groups; this was particularly evident in the ALIENOR cohort.

They concluded that their prediction model achieved high discrimination abilities and was a step toward precision medicine for patients with AMD.

More here:
The Use of Machine Learning to Forecast Progression to Advanced AMD - DocWire News

BOSTON, Sept. 14, 2020 /PRNewswire-PRWeb/ --RapidMiner, an enterprise AI platform for people of all skill levels, today announces that it has been recognized as a Leader in the Forrester Research, Inc. September 2020 report, The Forrester Wave: Multimodal Predictive Analytics And Machine Learning, Q3 2020.

This year's report evaluated 11 multimodal predictive analytics and machine learning (PAML) platforms based on 26 criteria, which are grouped into three high-level categories: current offering, strategy and market presence. Criteria for the platforms assessed include collaboration, model evaluation, model operations (ModelOps) and more. Of the platforms evaluated, RapidMiner received the highest possible scores in the modeling and model evaluation criteria, as well as in the ability to execute and solution roadmap criteria.

According to the report, "RapidMiner might not just have something for everyone; it could have everything for everyone." The report also notes, "[RapidMiner] has some of the most productivity-enhancing capabilities for automated data preparation (Turbo Prep) and model development (Auto Model) in the multimodal market, along with one of the most comprehensive visual tools for building data and ML pipelines."

RapidMiner is a data science platform that puts people not technology at the center of the enterprise AI journey. The platform empowers users of all domains and skillsets through a seamless combination of automated data science, drag and drop visual workflows, and notebook-based approaches. This allows business users, non-coding data scientists and coders to collaborate more effectively and work interchangeably.

"We believe that being named a Leader in this Wave evaluation from Forrester validates our commitment to reinvent enterprise AI so that anyone has the power to positively shape the future," said Peter Lee, CEO of RapidMiner. "By putting people at the center of the AI journey, we've developed a strong track record of helping companies in all major industries drive revenue, cut costs, and avoid risks."

In this evaluation, Forrester included vendors which, among other inclusion criteria, offer a solution that can operate on large data sets and provide capabilities for data acquisition and preparation, statistical and machine learning (ML) algorithms, a differentiated user interface to build models, and ModelOps features.

To read The Forrester Wave: Multimodal Predictive Analytics and Machine Learning, Q3 2020, visit https://rapidminer.com/resource/forrester-wave-predictive-analytics-machine-learning/.

About RapidMiner RapidMiner is reinventing enterprise AI so that anyone has the power to positively shape the future. We're doing this by enabling data loving people of all skill levels across the enterprise to rapidly create and operate AI solutions for immediate business impact. We offer a full lifecycle platform that unifies data prep, machine learning, and model operations with a user experience that provides depth for data scientists and simplifies complex tasks for everyone else. The RapidMiner Center of Excellence methodology and the RapidMiner Academy ensures customers are successful, no matter their experience or resource levels. More than 40,000 organizations in over 150 countries rely on RapidMiner to increase revenue, cut costs, and reduce risk. Learn more at rapidminer.com.

Media Contact: Zoe Cushman Matter Communications 617-874-5201 RapidMiner@matternow.com

SOURCE RapidMiner

Read more:
RapidMiner Named a Leader in Multimodal Predictive Analytics and Machine Learning Platforms by Independent Research Firm - Benzinga

Geoffrey Levene is the Director of Global Business Development for Data Science and Space at NVIDIA.

On this episode of the Connected Aircraft Podcast, we learn how airlines and aerospace manufacturers are adopting the use of data science workstations to develop task-specific machine learning models with Geoffrey Levene, Director, Global Business Development for Data Science and Space at NVIDIA.

In a May 7 blog, NVIDIA one of the worlds largest suppliers of graphics processing units and computer chips to the video gaming, automotive and other industries explained how American Airlines is using its data science workstations to integrate machine learning into its air cargo operations planning. During this interview, Levene expands on other airline and aerospace uses of those same workstations and how they are creating new opportunities for efficiency.

Have suggestions or topics we should focus on in the next episode? Email the host, Woodrow Bellamy atwbellamy@accessintel.com, or drop him a line on Twitter@WbellamyIIIAC.

Listen to this episode below, orcheck it out on iTunesorGoogle PlayIf you like the show, subscribe on your favorite podcast app to get new episodes as soon as theyre released.

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PODCAST: NVIDIA's Director of Data Science Talks Machine Learning for Airlines and Aerospace - Aviation Today

Further Education News

The FE News Channel gives you the latest education news and updates on emerging education strategies and the#FutureofEducation and the #FutureofWork.

Providing trustworthy and positive Further Education news and views since 2003, we are a digital news channel with a mixture of written word articles, podcasts and videos. Our specialisation is providing you with a mixture of the latest education news, our stance is always positive, sector building and sharing different perspectives and views from thought leaders, to provide you with a think tank of new ideas and solutions to bring the education sector together and come up with new innovative solutions and ideas.

FE News publish exclusive peer to peer thought leadership articles from our feature writers, as well as user generated content across our network of over 3000 Newsrooms, offering multiple sources of the latest education news across the Education and Employability sectors.

FE News also broadcast live events, podcasts with leading experts and thought leaders, webinars, video interviews and Further Education news bulletins so you receive the latest developments inSkills Newsand across the Apprenticeship, Further Education and Employability sectors.

Every week FE News has over 200 articles and new pieces of content per week. We are a news channel providing the latest Further Education News, giving insight from multiple sources on the latest education policy developments, latest strategies, through to our thought leaders who provide blue sky thinking strategy, best practice and innovation to help look into the future developments for education and the future of work.

In May 2020, FE News had over 120,000 unique visitors according to Google Analytics and over 200 new pieces of news content every week, from thought leadership articles, to the latest education news via written word, podcasts, video to press releases from across the sector.

We thought it would be helpful to explain how we tier our latest education news content and how you can get involved and understand how you can read the latest daily Further Education news and how we structure our FE Week of content:

Our main features are exclusive and are thought leadership articles and blue sky thinking with experts writing peer to peer news articles about the future of education and the future of work. The focus is solution led thought leadership, sharing best practice, innovation and emerging strategy. These are often articles about the future of education and the future of work, they often then create future education news articles. We limit our main features to a maximum of 20 per week, as they are often about new concepts and new thought processes. Our main features are also exclusive articles responding to the latest education news, maybe an insight from an expert into a policy announcement or response to an education think tank report or a white paper.

FE Voices was originally set up as a section on FE News to give a voice back to the sector. As we now have over 3,000 newsrooms and contributors, FE Voices are usually thought leadership articles, they dont necessarily have to be exclusive, but usually are, they are slightly shorter than Main Features. FE Voices can include more mixed media with the Further Education News articles, such as embedded podcasts and videos. Our sector response articles asking for different comments and opinions to education policy announcements or responding to a report of white paper are usually held in the FE Voices section. If we have a live podcast in an evening or a radio show such as SkillsWorldLive radio show, the next morning we place the FE podcast recording in the FE Voices section.

In sector news we have a blend of content from Press Releases, education resources, reports, education research, white papers from a range of contributors. We have a lot of positive education news articles from colleges, awarding organisations and Apprenticeship Training Providers, press releases from DfE to Think Tanks giving the overview of a report, through to helpful resources to help you with delivering education strategies to your learners and students.

We have a range of education podcasts on FE News, from hour long full production FE podcasts such as SkillsWorldLive in conjunction with the Federation of Awarding Bodies, to weekly podcasts from experts and thought leaders, providing advice and guidance to leaders. FE News also record podcasts at conferences and events, giving you one on one podcasts with education and skills experts on the latest strategies and developments.

We have over 150 education podcasts on FE News, ranging from EdTech podcasts with experts discussing Education 4.0 and how technology is complimenting and transforming education, to podcasts with experts discussing education research, the future of work, how to develop skills systems for jobs of the future to interviews with the Apprenticeship and Skills Minister.

We record our own exclusive FE News podcasts, work in conjunction with sector partners such as FAB to create weekly podcasts and daily education podcasts, through to working with sector leaders creating exclusive education news podcasts.

FE News have over 700 FE Video interviews and have been recording education video interviews with experts for over 12 years. These are usually vox pop video interviews with experts across education and work, discussing blue sky thinking ideas and views about the future of education and work.

FE News has a free events calendar to check out the latest conferences, webinars and events to keep up to date with the latest education news and strategies.

The FE Newsroom is home to your content if you are a FE News contributor. It also help the audience develop relationship with either you as an individual or your organisation as they can click through and box set consume all of your previous thought leadership articles, latest education news press releases, videos and education podcasts.

Do you want to contribute, share your ideas or vision or share a press release?

If you want to write a thought leadership article, share your ideas and vision for the future of education or the future of work, write a press release sharing the latest education news or contribute to a podcast, first of all you need to set up a FE Newsroom login (which is free): once the team have approved your newsroom (all content, newsrooms are all approved by a member of the FE News team- no robots are used in this process!), you can then start adding content (again all articles, videos and podcasts are all approved by the FE News editorial team before they go live on FE News). As all newsrooms and content are approved by the FE News team, there will be a slight delay on the team being able to review and approve content.

RSS Feed Selection Page

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How Can Machine Learning Help the Teaching Profession? - FE News

Banking and finance rely on experts but the new expert on the scene is your AI/ML combo, able to do far more, do it fast and do it accurately.

Making the right decisions and grabbing opportunities in the fast moving world of finance can make a difference to your bottom line. This is where artificial intelligence and machine learning make a tangential difference. Engage machine learning development services in your finance segment and life will not be the same. Markets and Markets study shows that artificial intelligence in financial segment will grow to over $ 7300 million by 2022.

Data

The simple reason you need machine learning development company to help you make better decisions with the help of AI/ML is data. Data flows in torrents from diverse sources and contains precious nuggets of information. This can be the basis of understanding customer behaviors and it can help you gain predictive capabilities. Data analysis with ML can also help identify patterns that could be indicative of attempts at fraud and you save your reputation and money by tackling it in time.

The key

Normalize huge sets of data and derive information in real time according to specifiable parameters. Machine Learning algorithms can help you train the system to carry out fast analysis and deliver results based on algorithm models created for the purpose by Machine Learning Development Company for you. As it ages the system actually becomes smarter because it learns as it goes along.

To achieve the same result manually using standard IT solutions you would employ a team of IT specialists but even then it is doubtful if you could get outputs in time to help you take decisive action.

Fraud prevention

This is one case where prevention is better than cure. A typical bank may have hundreds of thousands of customers carry out any number of different transactions. All such data is under the watchful eye of the ML imbued system and it is quick to detect anomalies. In fact, ML has been known to cause misunderstanding because a customer not familiar with credit card operations repeatedly fumbled and that raised a false alarm. Still, it is better to be safe than sorry and carry out firefighting after the event.

Stock trading

Day trading went algorithmic quite a few years back and helped brokers profit by getting the system to make automatic profitable trades. Apart from day trading there are derivatives, forex, commodities and binary where specific models for ML can help you, as a trader or a broker, anticipate price movements. This is one area where price is influenced not just by demand-supply but also by political factors, climate, company results and unforeseen calamities. ML keeps track of all and integrates them into a predictive capability to keep you ahead of the game.

Investment decisions

Likewise, investments in other areas like bonds, mutual funds and real estate need to be based on smart analysis of present and future while factoring external influencers. No one, for example, foresaw the covid-19 devastation that froze economies and dried up sources of funds that have an impact on investments, especially in real estate. However, if you have machine learning based system it would keep track of developments and alert you in advance so that you can be prepared. Then there are more mundane tasks in finance sector where ML does help. Portfolio managers always walk a tight rope and rely on experts who can make false decisions and affect clients capital. Tap into the power of ML to stay on top and grow wealth of wealthy clients. Their recommendations will get you more clients making the investment in ML solutions more than worthwhile. It could be the best investment you make.

Automation

Banks, private lenders, institutions and insurance companies routinely carry out repetitive and mundane tasks like attending to inquiries, processing forms and handling transactions. This does involve extreme manpower usage leading to high costs. Your employees work under a deluge of such tasks and cannot do anything productive. Switch to ML technologies to automate such repetitive tasks. You will have two benefits:

The second one alone is worth the investment. In the normal course of things you would have to devote considerable energies to identify developing patterns whereas the ML solution presents trends based on which you can modify services, design offers or address customer pain points and ensure loyalty.

Risk mitigation

Smart operators are always gaming the system such as finding ways to improve credit score and obtain credit despite being ineligible. Such operators would pass the normal scanning technique of banks. However, if you have ML for assessment of loan application the system delves deeper and digs to find out all relevant information, collate it and analyze it to help you get a true picture. Non-performing assets cause immense losses to banks and this is one area where Machine Learning solutions put in place by expert machine learning development services can and does prove immensely valuable.

Banking and finance rely on experts but the new expert on the scene is your AI/ML combo, able to do far more, do it fast and do it accurately.

Link:
Finance Sector Benefits from Machine Learning Development and AI - Legal Reader

Because of the popularity of MOFs, scientists are developing, synthesizing, studying, and cataloging MOFs. However, the sheer number of MOFs is creating a problem.

Even if synthesizing new MOF, it is quite challenging to know whether it is new and not some minor variation of a structure that has already been synthesized.

To address this problem, EPFL scientists, in collaboration with MIT, have used machine-learning to organize the chemical diversity found in the ever-growing databases for the popular metal-organic framework materials. Using machine learning, scientists developed a language to compare two materials and quantify their differences.

Through this new language, scientists set off to determine the chemical diversity in MOF databases.

Professor Berend Smit at EPFL said,Before, the focus was on the number of structures. But now, we discovered that the major databases have all kinds of bias towards particular structures. There is no point in carrying out expensive screening studies on similar structures. One is better off in carefully selecting a set of very diverse structures, which will give much better results with far fewer structures.

Another exciting application is scientific archeology: The researchers used their machine-learning system to identify the MOF structures that, at the time of the study, were published as very different from the ones that are already known.

Smit said,So we now have a straightforward tool that can tell an experimental group how different their novel MOF is compared to the 90,000 other structures already reported.

See the article here:
Using machine learning to organize the chemical diversity - Tech Explorist