Category Archives: Machine Learning
The Top Machine Learning WR Prospect Will Surprise You – RotoExperts
What Can Machine Learning Tell Us About WR Prospects?
One of my favorite parts of draft season is trying to model the incoming prospects. This year, I wanted to try something new, so I dove into the world of machine learning models. Using machine learning to detail the value of a WR prospect is very useful for dynasty fantasy football.
Machine learning leverages artificial intelligence to identify patterns (learn) from the data, and build an appropriate model. I took over 60 different variables and 366 receiving prospects between the 2004 and 2016 NFL Drafts, and let the machine do its thing. As with any machine, some human intervention is necessary, and I fine-tuned everything down to a 24-model ensemble built upon different logistic regressions.
Just like before, the model presents the likelihood of a WR hitting 200 or more PPR points in at least one of his first three seasons. Here are the nine different components featured, in order of significance:
This obviously represents a massive change from the original model, proving once again that machines are smarter than humans. I decided to move over to ESPN grades and ranks instead of NFL Draft Scout for a few reasons:
Those changes alone made strong improvements to the model, and it should be noted that the ESPN overall ranks have been very closely tied to actual NFL Draft position.
Having an idea of draft position will always help a model since draft position usually begets a bunch of opportunity at the NFL level.
Since the model is built on drafts up until 2016, I figured perhaps youd want to see the results from the last three drafts before seeing the 2020 outputs.
It is encouraging to see some hits towards the top of the model, but there are obviously some misses as well. Your biggest takeaway here should be just how difficult it is to hit that 200 point threshold. Only two prospects the last three years have even a 40% chance of success. The model is telling us not to be over-confident, and that is a good thing.
Now that youve already seen some results, here are the 2020 model outputs.
Tee Higgins as the top WR is likely surprising for a lot of people, but it shouldnt be. Higgins had a fantastic career at Clemson, arguably the best school in the country over the course of his career. He is a proven touchdown scorer, and is just over 21 years old with a prototypical body-type.
Nobody is surprised that the second WR on this list is from Alabama, but they are likely shocked to see that a data-based model has Henry Ruggs over Jerry Jeudy. The pair is honestly a lot closer that many people think in a lot of the peripheral statistics. The major edge for Ruggs comes on the ground. He had a 75 yard rushing touchdown, which really underlines his special athleticism and play-making ability.
The name that likely stands out the most is Geraud Sanders, who comes in ahead of Jerry Jeudy despite being a relative unknown out of Air Force. You can mentally bump him down a good bit. The academy schools are a bit of a glitch in the system, as their offensive approach usually yields some outrageous efficiency. Since 2015, 12 of the top 15 seasons in adjusted receiving yards per pass attempt came from either an academy school or Georgia Techs triple-option attack. Sanders isnt a total zero, his profile looks very impressive, but I would have him closer to a 10% chance of success given his likely Day 3 or undrafted outcome in the NFL Draft.
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The Top Machine Learning WR Prospect Will Surprise You - RotoExperts
Harnessing the latest machine learning and artificial intelligence technologies to create and improve education and assessment solutions for lifelong…
#MachineLearning and #AI ArtificialIntelligence for #LifelongLearning -RM Results launches RM Studio to accelerate #EdTech innovation as part of a wider drive to transform the education landscape
RM Results, the digital assessment solutions business that works with leading exam boards and educational institutions across the globe, has launched its own in-house innovation lab. RM Studio is driving the continuous development of new and existing products and services, harnessing the latest technologies including machine learning and artificial intelligence to create and improve education and assessment solutions for lifelong learning.
The overarching aim of RM Studio is to design and develop solutions that make education and assessment a more positive experience for all those involved, from learners to assessors, awarding organisations to educational institutions.
RM Studio uses tried-and-tested start-up methods to accelerate projects. When a need or opportunity to add value has been identified, either within RM Results or through their discussions with students, educators, and awarding bodies, solutions are proposed until the most viable is settled on. After this, various innovation tools and methods are utilised, and the team are coached on how to best manage and progress their innovations. A minimum viable product is designed, and feedback is used to develop it further.
The new initiative is spearheaded by Roberto Hortal, who has been building the RM Studio innovation team leading a design-thinking approach across the business, since his appointment to the newly-created Head of Innovation role in January 2019. RM Studio works closely with customers including Cambridge Assessment, the International Baccalaureate and SQA to understand and anticipate the needs of the assessment sector now and in the future.
Prior to joining RM in 2019, Roberto gained over two decades of experience in implementing innovation programmes, having previously been responsible for significant first ever digital milestones at Nokia, easyJet, MORE TH>N, EDF Energy and Co-Op Group. This increased investment from RM Results in innovation marks the companys commitment to a more direct, mature approach to innovation, and is part of its continuing efforts to drive the global modernisation of assessment.
A key aspect of RM Studio is creating a culture of innovation to further the individual empowerment of employees, offering them opportunities to pursue their own ideas and, potentially, see them developed and added to the RM product suite.
Roberto Hortal, Head of Innovation at RM Results, says:
The landscape of education looks nothing like it did twenty years ago. Education and technology are now inextricably linked, and increasingly we are seeing people engaging with education throughout their lives, rather than just their school and university years. As the world of education diversifies, we want to be providing cutting edge solutions for markets as they emerge and grow. We firmly believe that, by focusing our innovative efforts through a structured, supportive pipeline, RM Studio is precisely what will allow us to achieve this.
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He adds:
Education and technology are intersecting in all sorts of ways, from wearable devices, to remote teaching, to artificial intelligence. New opportunities are constantly presenting themselves, and educators are always on the lookout for solutions that offer flexibility and make their jobs and lives easier. We want to enable the best lifelong learning opportunities for everyone.
Richard Little, Product Development Director at RM Results, commented:
The addition of a Head of Innovation to our team, and subsequent launch of RM Studio, has allowed us to push forward with a new wave of initiatives, and is perfectly timed as we prepare to launch new products. At RM Results, everyone is encouraged to innovate, it is a celebrated part of our culture. Robertos expertise and experience in successfully bringing innovation to various industries means he is the perfect figurehead to lead our ambitious plans.
While RM Studio operates in-house, RM Results is keen to explore opportunities for partnerships and open innovation, and in doing so bring the collaborative approach of RM Studio to their relationships with others.
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Facebook, YouTube, and Twitter warn that AI systems could make mistakes – Vox.com
To adjust to the social distancing required by the Covid-19 coronavirus pandemic, social media platforms will lean more heavily on artificial intelligence to review content that potentially violates their policies. That means your next YouTube video or snarky tweet might be more likely to get taken down in error.
As they transition their operations to a primarily work-from-home model, platforms are asking users to bear with them while acknowledging that their automated technology will probably make some mistakes. YouTube, Twitter, and Facebook recently said that their AI-powered content moderators may be overly aggressive in flagging questionable content and encouraged users to be vigilant about reporting potential mistakes.
In a blog post on Monday, YouTube told its creators that the platform will turn to machine learning to help with some of the work normally done by reviewers. The company warned that the transition will mean that some content will be taken down without human review, and that both users and contributors to the platform might see videos removed from the site that dont actually violate any of YouTubes policies.
The company also warned that unreviewed content may not be available via search, on the homepage, or in recommendations.
Similarly, Twitter has told users that the platform will increasingly rely on automation and machine learning to remove abusive and manipulated content. Still, the company acknowledged that artificial intelligence would be no replacement for human moderators.
We want to be clear: while we work to ensure our systems are consistent, they can sometimes lack the context that our teams bring, and this may result in us making mistakes, said the company in a blog post.
To compensate for potential errors, Twitter said it wont permanently suspend any accounts based solely on our automated enforcement systems. YouTube, too, is making adjustments. We wont issue strikes on this content except in cases where we have high confidence that its violative, the company said, adding that creators would have the chance to appeal these decisions.
Facebook, meanwhile, says its working with its partners to send its content moderators home and to ensure that theyre paid. The company is also exploring remote content review for some of its moderators on a temporary basis.
We dont expect this to impact people using our platform in any noticeable way, said the company in a statement on Monday. That said, there may be some limitations to this approach and we may see some longer response times and make more mistakes as a result.
The move toward AI moderators isnt a surprise. For years, tech companies have pushed automated tools as a way to supplement their efforts to fight the offensive and dangerous content that can fester on their platforms. Although AI can help content moderation move faster, the technology can also struggle to understand the social context for posts or videos and, as a result make inaccurate judgments about their meaning. In fact, research has shown that algorithms that detect racism can be biased against black people, and the technology has been widely criticized for being vulnerable to discriminatory decision-making.
Normally, the shortcomings of AI have led us to rely on human moderators who can better understand nuance. Human content reviewers, however, are by no means a perfect solution either, especially since they can be required to work long hours analyzing incredibly traumatic, violent, and offensive words and imagery. Their working conditions have recently come under scrutiny.
But in the age of the coronavirus pandemic, having reviewers working side by side in an office could not only be dangerous for them, it could also risk further spreading the virus to the general public. Keep in mind that these companies might be hesitant to allow content reviewers to work from home as they have access to lots of private user information, not to mention highly sensitive content.
Amid the novel coronavirus pandemic, content review is just another way were turning to AI for help. As people stay indoors and look to move their in-person interactions online, were bound to get a rare look at how well this technology fares when its given more control over what we see on the worlds most popular social platforms. Without the influence of human reviewers that weve come to expect, this could be a heyday for the robots.
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Facebook, YouTube, and Twitter warn that AI systems could make mistakes - Vox.com
Global Machine Learning in Communication Market 2020 Industry Trend and Forecast 2024 – Daily Science
The study on Global Machine Learning in Communication Market, offers deep insights about the Machine Learning in Communication market covering all the crucial aspects of the market. Moreover, the report provides historical information with future forecast over the forecast period. Some of the important aspects analyzed in the report includes market share, production, key regions, revenue rate as well as key players. This Machine Learning in Communication report also provides the readers with detailed figures at which the Machine Learning in Communication market was valued in the historical year and its expected growth in upcoming years. Besides, analysis also forecasts the CAGR at which the Machine Learning in Communication is expected to mount and major factors driving markets growth.
Key vendors/manufacturers in the market:
Market Segment by Companies, this report coversAmazonIBMMicrosoftGoogleNextivaNexmoTwilioDialpadCiscoRingCentral
Request a sample of this report @ https://www.orbisresearch.com/contacts/request-sample/3063584
The Global Machine Learning in Communication Market is a highly competitive market. It has some players who have been in the business for quite some time. Subsequently there are many startups coming up to seize the huge opportunity this market offers. Some players have a presence only in a particular geography. In addition, the projections offered in this report have been derived with the help of proven research assumptions as well as methodologies. By doing so, the Machine Learning in Communication research study offers collection of information and analysis for each facet of the Machine Learning in Communication market such as technology, regional markets, applications, and types. Likewise, the Machine Learning in Communication market report offers some presentations and illustrations about the market that comprises pie charts, graphs, and charts which presents the percentage of the various strategies implemented by the service providers in the Global Machine Learning in Communication Market. In addition to this, the report has been designed through the complete surveys, primary research interviews, as well as observations, and secondary research.
Likewise, the Global Machine Learning in Communication Market report also features a comprehensive quantitative and qualitative evaluation by analyzing information collected from market experts and industry participants in the major points of the market value chain. This study offers a separate analysis of the major trends in the existing market, orders and regulations, micro & macroeconomic indicators is also comprised in this report. By doing so, the study estimated the attractiveness of every major segment during the prediction period.
Browse the complete report @ https://www.orbisresearch.com/reports/index/global-machine-learning-in-communication-market-2019-by-company-regions-type-and-application-forecast-to-2024
Global Machine Learning in Communication Market by Type:
Market Segment by Type, coversCloud-BasedOn-Premise
Global Machine Learning in Communication Market by Application:
Market Segment by Applications, can be divided intoNetwork OptimizationPredictive MaintenanceVirtual AssistantsRobotic Process Automation (RPA)
The Global Machine Learning in Communication Market has its impact all over the globe. On Global Machine Learning in Communication industry is segmented on the basis of product type, applications, and regions. It also focusses on market dynamics, Machine Learning in Communication growth drivers, developing market segments and the market growth curve is offered based on past, present and future market data. The industry plans, news, and policies are presented at a global and regional level.
Major Table of Contents1 Machine Learning in Communication Market Overview2 Company Profiles3 Market Competition, by Players4 Market Size by RegionsContinued
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Global Machine Learning in Communication Market 2020 Industry Trend and Forecast 2024 - Daily Science
AI, machine learning to deliver ‘wave of discoveries’ – The Northern Miner
The past 20 years have seen remarkable advances in the mining industry, particularly in mineral exploration technologies with vast volumes of data generated from geologic, geophysical, geochemical, satellite and other surveying techniques. However, the abundance of data has not necessarily translated into the discovery of new deposits, according to Colin Barnett, co-founder of BW Mining, a Boulder, Colorado-based data mining and mineral exploration company.
One of the problems were facing in exploration is the huge increase in the amounts of data we have to look at, said Barnett, in his presentation at theManaging and exploring big data through artificial intelligence and machine learning session at the recent PDAC 2020 convention in Toronto. And although its high-quality data, the sheer volume is becoming almost overwhelming for human interpreters, and so we need help in getting to the bottom of it.
By integrating hundreds or even thousands of interdependent layers of data, with each layer making its own statistically determined contribution, machine learning offers a solution to the problem of tackling the massive amounts of data generated, and a powerful new tool in the search for mineral deposits.
But, in an interview with The Northern Miner, he cautioned that to fully exploit the potential of machine learning in mineral exploration, prospectors will still need to devote considerable time and effort to the preparation of data before machine learning techniques can add value for companies.
To illustrate his point, Barnett demonstrated how he and his partner at BW Mining, Peter Williams, are using machine learning to analyze data from geological, geochemical and geophysical surveys of the Yukon in northwestern Canada to locate new deposits.
The Yukon became famous for the Klondike gold rush during the late 1890s, which petered out after a few years as prospectors moved onto Alaska. Today the area is experiencing a renewed interest in what has become known as the Tintina Gold Belt, with significant lode deposits being found over the past two decades and, according to Barnett, more waiting to be discovered.
We used the Yukon bedrock geology map published by the Yukon Geological Survey, which is very detailed and shows over 200 different geological formations, explained Barnett. However, you cant simply put 200 formations into a machine learning process. First, the data requires special treatment.
By representing each of the formations with a separate grid and by continuing the grids upward, they were able to see overlaps between formations, allowing them to consolidate the data by grouping the formations by rock type and age, and thereby reducing the data set down to around 50 discrete and different formations. They then used the same process to represent structural data provided by the map.
The structural data is important because it represents the pathways that the mineralization generally took to reach the surface, explained Barnett. We then used geophysical maps of the area provided by Natural Resources Canada, which contain enormous amounts of information that can be extracted and subjected to the same statistical treatment.
Applying the same approach to geochemical, gravity, topographical and satellite data, they were able to generate detailed data sets covering over 300,000 400,000 square kilometers of the study area.
The most critical layer of data for our machine learning process is the known deposits because this is used to train our artificial neural network against all the other layers to identify deposit formations, said Barnett.
Artificial neural networks operate much like the human brain. They can recognize patterns in the different layers of data and cluster or classify them into groups according to similarities in the input data. They are then capable of discriminating between zones of high and low mineral potential.
After scouring through geologic publications, company websites and NI 43-101 technical reports, Barnett and Williams were able to develop accurate mineral footprints for more than 30 deposits using their model, which, according to Barnett, reportedly contain over 46 million oz. of gold.
They then used an artificial neural network to establish the statistical favourability of a location containing an economically viable deposit across the entire region of interest. This approach is essentially an inversion process that uses exploration data relating to a given location as inputs to the network, which then produces the corresponding favourability as the output.
Image of a typical target. Red areas are highly favourable, while purple areas are shown as unfavourable for gold. Credit: BW Mining.
This requires very sophisticated software to analyze and interpret the data, so you cant just use off-the-shelf software, explained Barnett. We first started analyzing the data on a parallel-processor in the basement of the University of Sussex [in England] back in 1992, where my partner was a professor. But it would take five days to get an answer by which time wed forgotten what the question was.
However, with improvements to computer software and hardware, they are now able to generate an answer in a matter of hours using a common laptop.
Barnetts and Williams use of artificial intelligence and machine learning has led to a highly-focused target map that assigns numerical probabilities of making an economic discovery anywhere in the region of interest, and can be used to systematically rank and rate targets and plan cost-effective follow-up programs that take into account the expected return on investment for any given target.
Although Barnett believes there is currently a lack of understanding of artificial intelligence and machine learning in the industry, he is convinced that as these techniques become more widely used and available, machine learning and artificial intelligence will lead to a wave of discoveries. And within ten years, they will be commonly used tools in the mineral exploration industry.
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AI, machine learning to deliver 'wave of discoveries' - The Northern Miner
Brain Computer Interface: Definitions, Tools and Applications – AiThority
Weve finally reached a stage in our technical expertise where we can think about connecting our minds with machines. This is possible through brain-computer interface (BCI) technologies that would soon transcend our human capabilities.
The human race is looking at the past to create future tomorrows would be controlled by your mind, and machines will be your agents. If we look into the recent advancements in Computing, Data Science, Machine Learning and Neural Networking, the future looks very predictable, yet disarmingly tough. Imagine the future like this Were moving into a latent telepathy mode very soon. Its truly going to be a brain-power that will operate machines and get work done, AI or no AI.
In this article, we will quickly summarize the Brain-Computer Interface (BCI) definitions, key technologies, and their applications in the modern Artificial Intelligence age.
A Brain-Computer Interface can be defined as a seamless network mechanism that relays brain activity into a desired mechanical action. A modern BCI action would involve the use of a brain-activity analyzer and neural networking algorithm that acquires complex brain signals, analyzes them, and translates them for a machine. These machines could be a robotic arm, a voice box, or any automated assistive device such as prosthetics, wheelchair, and iris-controlled screen cursors.
This is a simple infographic about BCIs.
Advancements in functional neuroimaging techniques and inter-cranial Spatial imagery have opened up new avenues in the fields of Cognitive Learning and Connected Neural Networking. Today, Brain-Computer Interfaces rely on a mix of signals acquired from the brain and nervous systems. These are classified under
According to the US National Library of Medicine National Institutes of Health, there are three types of BCI technologies. These are
Brain-Computer Interface is used to complete a mental task using neuro-motor output pathways or imagery. For example, lifting your leg to climb steps.
This is a stimulus-based conditional Brain-Computer Interface that acts on selective attention. For example, crouching on your feet to cross a barbed fence. The principle behind Reactive BCIs can be better understood from the P300 settings. The P300 setting involves a mix of neuroscience-based decision making and cognitive learning based on visual stimulus.
It involves no visual stimulus. The BCI mechanism merely acts like a switch (On/Off) based on the cognitive state of the brain and body at work. It is the least researched category in BCI development.
Unlike general Cloud Computing and Machine Learning DevOps, the BCI developers come with a specialized background.
Hot Start-Ups:TIBCO Recognized as a Leader in 2020 Gartner Magic Quadrant for Data Science and Machine Learning Platforms
Brain-Computer Interface DevOps engineers have to constantly work with a team of Neuroscientists, Computer Programmers, Neurologists, Psychologists, Rehabilitation Specialists, and sometimes, Camera OEMs.
According to a paper on Brain-computer interfaces for communication and control, BCIs in 2002 could deliver maximum information transfer rates up to 10-25bits/min.
Since then, BCI development has gained major traction from large-scale innovation companies and futurist technocrats such as Teslas Elon Musk. We are already seeing logic-defying amalgamation of AI research and interdisciplinary collaboration between Neurobiology, Psychology, Engineering, Mathematics, and Computer Science.
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Brain Computer Interface: Definitions, Tools and Applications - AiThority
Researchers combine low-cost tactile sensor with machine learning to develop robots that feel – SlashGear
Researchers from ETH Zrich have announced that they have leveraged machine learning to develop a low-cost tactile sensor. The sensor can measure force distribution in high resolution and with high accuracy. Those features enable the robot arm to grasp sensitive, fragile objects with more dexterity. Enabling robotic grippers to feel is very important to making them more efficient.
In humans, our sense of touch allows us to pick up fragile or slippery items with our hands without fear of crushing or dropping the item. If an object is about fall through our fingers, we are able to adjust the strength of our group accordingly. Scientists want robotic grippers that pick-up products to have a similar type of feedback as humans get from our sense of touch. The new sensor that the researchers have created is said to be a significant step towards a robotic skin.
The sensor consists of an elastic silicone skin with colored plastic microbeads and a regular camera fixed to its underside. The vision-based sensor can see when it comes in contact with an object, and an indentation appears in the silicone skin. The contact changes the pattern of the microbeads that can be registered by the fisheye lens on the underside of the sensor. Changes in the pattern of the microbeads is used to calculate the force distribution on the sensor.
The robotic skin the scientists came up with can distinguish between several forces acting on the sensor surface and calculate them with high degrees of resolution and accuracy. The team can determine the direction from which the force is acting. When calculating the forces that are pushing the microbeads and in which directions, the team uses a set of experiments and data.
This approach allows the team to precisely control and systematically vary the location of the contact, the force distribution, and the size of the object making contact. Machine learning allows the researchers to record several thousand instances of contact and precisely match them with changes in the bead pattern. The team is also working on larger sensors that are equipped with several cameras and can recognize objects of complex shape. They are also working to make the sensor thinner.
Alibaba using machine learning to fight coronavirus with AI – Gigabit Magazine – Technology News, Magazine and Website
Chinese ecommerce giant Alibaba has announced a breakthrough in natural language processing (NLP) through machine learning.
NLP is a key technology in the field of speech technologies such as machine translation and automatic speech recognition. The companys DAMO academy, a global research program, has made a breakthrough in machine reading techniques with applications in the fight against coronavirus.
Alibaba not only topped the GLUE Benchmark rankings, a table measuring the performance of competing NLP models, despite competition from the likes of Google, Facebook and Microsoft, but beat human baselines, signifying that its model could even outperform a human at understanding language. Applications include sentiment analysis, textual entailment (i.e. understanding the correct chronology of sentences) and question-answering.
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With the solution already deployed in technologies ranging from AI chatbots to search engines, it is now finding use in the analysis of healthcare records by centers for disease control in cities across China.
We are excited to achieve a new breakthrough in driving research of the NLP development, said Si Luo, head of NLP Research at Alibaba DAMO Academy. Not only NLP as a core technology underpinning Alibabas various businesses, which serve hundreds of millions of customers, but it also becomes a critical technology now in fighting the coronavirus. We hope we can continue to leverage our leading technologies and contribute to the community during this difficult time.
Other AI initiatives put forth by the company for use in containing the coronavirus epidemic include technology to assist in the diagnosis of the virus. The company also made its Alibaba Cloud computing platform free for research organisations seeking to sequence the virus genome.
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Alibaba using machine learning to fight coronavirus with AI - Gigabit Magazine - Technology News, Magazine and Website
AI and machine learning is not the future, it’s the present – Eyes on APAC – ComputerWeekly.com
This is a guest post by Raju Vegesna, chief evangelist at Zoho
For many, artificial intelligence (AI) is a distant and incomprehensible concept associated only with science fiction movies or high-tech laboratories.
In reality, however, AI and machine learning is already changing the world we know. From TVs and toothbrushes to real-time digital avatars that interact with humans, the recent CES show demonstrated how widespread AI is becoming in everyday life.
The same can be said of the business community, with the latest Gartner research revealing that 37% of organisations had implemented some form of AI or machine learning.
So far, these technologies have largely been adopted and implemented more by larger organisations with the resources and expertise to seamlessly integrate them into their business. But technology has evolved significantly in recent years, and SaaS (software as a service) providers now offer integrated technology and AI that meets the needs and budgets of small and medium businesses too.
Here are a few evolving trends in AI and machine learning that businesses of all sizes could capitalise on in 2020 and beyond.
The enterprise software marketplace is expanding rapidly. More vendors are entering the market, often with a growing range of solutions, which creates confusion for early adopters of the technology. Integrating new technologies from a range of different vendors can be challenging, even for large enterprise organisations.
So, in 2020 and beyond, the businesses that will make the most of AI and machine learning are the ones implementing single-vendor technology platforms. Its a challenge to work with data that is scattered across different applications using different data models, but organisations that consolidate all its data in one integrated platform will find it much easier to feed it into a machine learning algorithm.
After all, the more data thats available, the more powerful your AI and machine learning models will be. By capitalising on the wealth of data supplied by integrated software platforms, advanced business applications will be able to answer our questions or help us navigate interfaces. If youre a business owner, planning to utilise AI and machine learning for your business in 2020, then the single-vendor strategy is the way to go.
Technology has advanced at such a rate that businesses no longer need to compromise to fit the technology. This type of hyper-personalisation increases productivity for business software users and will continue to be a prime focus for businesses in 2020.
Take, for example, the rise of algorithmic social media timelines we have seen in the last few years. For marketers, AI and machine learning mean personalisation is becoming more and more sophisticated, allowing businesses to supercharge and sharpen their focus on their customers. Companies which capture insights to create personalised customer experiences and accelerate sales will likely win in 2020.
With AI and machine learning, vast amounts of data is processed every second of the day. In 2020, one of the significant challenges faced by companies implementing AI and machine learning is data cleansing the process of detecting, correcting or removing corrupt or inaccurate records from a data set.
Smaller organisations can begin to expect AI functionality in everyday software like spreadsheets, where theyll be able to parse information out of addresses or clean up inconsistencies. Larger organisations, meanwhile, will benefit from AI that ensures their data is more consumable for analytics or prepares it for migration from one application to another.
Businesses can thrive with the right content and strategic, innovative marketing. Consider auto-tagging, which could soon become the norm. Smartphones can recognise and tag objects in your photos, making your photo library much more searchable. Well start to see business applications auto-tag information to make it much more accessible.
Thanks to AI, customer relationship management (CRM) systems will continue to be a fantastic and always-advancing channel through which businesses can market to their customers. Today, businesses can find its top customers in a CRM system by running a report and sorting by revenue or sales. In the coming years, businesses will be able to search top customers, and its CRM system will know what theyre looking for.
With changing industry trends and demands, its important for all businesses to use the latest technology to create a positive impact on its operations. In 2020 and beyond, AI and machine learning will support businesses by helping them reduce manual labour and enhance productivity.
While some businesses, particularly small businesses, might be apprehensive about AI, it is a transformation that is bound to bring along a paradigm shift for those that are ready to take a big step towards a technology-driven future.
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AI and machine learning is not the future, it's the present - Eyes on APAC - ComputerWeekly.com
AI in the Translation Industry The 5-10 Year Outlook – AiThority
Artificial intelligence(AI) has had a major and positive impact on a range of industries already, with the potential to give much more in the future. We sat down with Ofer Tirosh, CEO ofTomedes, to find out how the translation industry has changed as a result of advances in technology over the past 10 years and what the future might hold in store for it.
Translation services have felt the impact of technology in various positive ways during recent years. For individual translators, the range and quality of computer-assisted translation (CAT) tools have increased massively. A CAT tool is a piece of software that supports the translation process. It helps the translator to edit and manage their translations.
CAT tools usually include translation memories, which are particularly valuable to translators. They store sentences and their translations for future use and can save a vast amount of time during the translation process. This means that translators can work more efficiently, without compromising on quality.
There are myriad other ways that technology has helped the industry. Everything from transcription to localization services has become faster and better as a result of tech advances. Even things likecontract automationmake a difference, as they speed up the overall time taken to set up and deliver on each client contract.
Also Read:Top 9 SaaS Startups in India 2020
Machine translation is an issue that affects not just our translation agency but the industry as a whole. Human translation still outdoes machine translation in terms of quality but the fact that websites that can translate for free are widely available has tempted many companies to try machine translation. The resulting translations are not good qualitand this acceptance of below-par translations isnt great for the industry as a whole, as it drives down standards.
There were some fears around machine translation taking over from professional translation services whenmachine learningwas first used to move away from statistical-based machine translation. However, those fears havent really materialized. Indeed, the Bureau of Labor Statistics is projecting19% growthfor the employment of interpreters and translators between 2018 and 2028, which is well above the average growth rate.
Instead, the industry has adapted to work alongside the new machine translation technology, with translators providing post-editing machine translation services, which essentially tidy up computerized attempts at translation and turn them into high-quality documents that accurately reflect the original content.
It was the introduction of neural networks that really took machine language learning to the next level. Previously, computers relied on the analysis of phrases (and before that, words) from existing human translations in order to produce a translation. The results were far from ideal.
Neural networks have provided a different way forward. A machine learning algorithm is used so that the machine can explore the data in its own way, learning and progressing in ways that were not previously possible. What is particularly exciting about this approach is the adaptability of the model that the machine creates. Its not a static process but one that can flex and change over time and based on new data.
Also Read:Vectors of Innovation with Conversational AI
I think the fears of machines taking over from human translation professionals have been put to bed for now. Yes, machines can translate better than they used to, but they still cant translate as well as humans can.
I think that well see a continuation of the trend towards more audio and video translation. Video, in particular, has become such an important marketing and social connection tool that demandvideo translationis likely to boom in the years ahead, just as it has for the past few years.
Ive not had access yet to anyPredictive Intelligencedata for the translation industry, unfortunately, but were definitely likely to experience an increase in demand for more blended human and machine translation models over the coming years. Theres an increasing need to translate faster without a drop in quality, for example in relation to thespread of coronavirus. We need to ensure a smooth, rapid flow of accurate information from country to country in order to tackle the situation as a global issue and not a series of local ones. Thats where both machines and humans can support the delivery of high quality, fast translation services, by working together to achieve maximum efficiency.
AI has had a major impact on the translation industry over the past ten years and I expect the pace of change over the next ten to be even greater, as the technology continues to advance.
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Original post:
AI in the Translation Industry The 5-10 Year Outlook - AiThority