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TAIPEI, TAIWAN\u00a0<\/b>–\u00a0Media OutReach<\/a>\u00a0–\u00a023 February 2021 –<\/span>\u00a0<\/b>Appier<\/a>, a leading
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Larger models have
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In parallel, we’ve
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The E-Commerce Boom Is AI-Driven<\/b><\/p>\n
Over the last
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AI gives marketers a way to influence customer’s behavior at a
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Data-Driven Finance Relies on AI<\/b><\/p>\n
Furthermore, the main application of AI in finance has been in
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While high-frequency trading strategies are important, there is another
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AI in Healthcare and Biomedical Research<\/b><\/p>\n
The prototype of messenger RNA (mRNA) COVID-19 vaccines was developed in
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In the biomedical
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Machine learning
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The Future of Education<\/b><\/p>\n
Curricula and textbooks have typically been developed to
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For example, a student may be very strong with fractions
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With so much content now available online, cheating and
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Smart Farming and
Factories and farms are using data in innovative ways too.
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The biggest development we are seeing in this area is in
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Perhaps the biggest prediction we can make
\nartificial intelligence (AI) company, today shares AI Predictions and Trends to
\nWatch in 2021. Artificial intelligence (AI) and machine learning (ML)
\nhave moved from the backrooms of computer science into the mainstream. Their
\nimpact is being felt in everything from how we shop through to finance markets
\nand medical research, as well as the agriculture and manufacture industries.<\/p>\n
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\nbeen trained in separated modality. For instance, GPT-3 is the first
\n100-billion-parameter model for natural language processing (NLP). Recently,
\na-trillion-parameter model (T5-XXL) has also been trained. They can be used to
\nwrite articles, analyze text, perform translations and even create poetry. <\/p>\n
\n
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\nseen models used for image recognition and generation greatly improved as they
\nhave also been trained with more data sets. What we are seeing emerge is the
\npower that can come from combining two or more AI models without changing these
\nlarge models. In this way, combining these large models becomes affordable.
\nThat will allow us to use AI to interpret text and generate a completely new
\nimage. The following are the current observations and predictions of AI
\napplications in five major fields.<\/p>\n
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\nyear,\u00a0online commerce has grown
\nsignificantly<\/a> and is expected to continue to increase. COVID-19
\nrestrictions have resulted in people spending much more time online — not just
\nshopping but in online meetings, playing games, accessing social media and
\nusing apps. The growing digital journeys undertaken by people have generated
\nmore data that can be used to understand human behavior. However, more data
\nalso brings a greater complexity. Today, there’s no single, most effective
\nchannel for reaching customers. Reaching the right customer on the right
\nchannel at the right time is complicated for humans, but that complexity can be
\novercome through the use of AI. <\/p>\n
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\npace and scale previously thought impossible. AI not only finds the right
\ncustomers, but also accesses the often-forgotten long tail of customers. It can
\nalso effectively generate creatives and develop customized content for
\ndifferent customers, and test the performance for different creatives to
\nincrease user engagement.\u00a0 <\/p>\n
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\nhigh-frequency trading where transactions are conducted between machines faster
\nthan people can communicate. This will continue in both traditional finance and
\nin the world of cryptocurrencies, where we see different AIs engage in
\n‘warfare’. Investors have been using AI to make long-term predictions — which
\nhas required systems that can understand investors’ long-term targets. These
\nwere typically centered around measures such as revenues, incomes and profits. <\/p>\n
\n
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\nfactor to show that cryptocurrencies are far more challenging to predict. Much
\nof what we see in cryptocurrency markets is driven by ‘human madness’. While AI
\nmodels struggle with this today, we can expect the AI models of the future to
\nevolve and do a better job of predicting this behavior through closely
\nmonitoring trends in media and social networks.<\/p>\n
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\ndays thanks to the digitization tools of genetic code sequencing and the
\ntranscription tools of making mRNA from genetic code sequence. With the help of
\nAI to predict new mutations in the Sars-Cov-2 virus, the process of developing
\nmRNA vaccines will be even faster. AI can also be used as a diagnostic tool to read x-rays<\/a>, based on the sound of someone
\ncoughing and indicate whether the patient is likely to be suffering from
\nCOVID-19 or some other illness.<\/p>\n
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\ndomain, sequences of codes, such as DNA or amino acid, are commonly used. Since
\nsequences of codes can be treated as a type of language with hidden structure,
\nthe architecture used in NLP models can be potentially used to understand and
\ngenerate sequences of codes in the biomedical domain as well. One impressive
\nexample in early 2021 is that biomedical researchers used language model
\narchitecture to predict virus mutations and to understand protein folding — a
\nkey challenge in the creation of some of the vaccines now available. This
\nfinding is actually adapting the architecture of one model to solve problems in
\nthe biomedical domain.<\/p>\n
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\nand AI don’t replace clinicians and researchers; they allow these professionals
\nto work faster and rapidly test hypotheses. Instead of waiting for cell
\ncultures to grow in the physical world, they can use these models to understand
\nwhat will happen much faster in the digital simulation. As more and more
\npeople wear devices that can monitor heart rate, body temperature, blood
\npressure and other critical factors, the data can be used to give doctors
\ngreater insight into a patient’s condition. It also aids accuracy when making
\ndiagnoses as doctors and other clinicians are no longer reliant on patient
\nrecollections.<\/p>\n
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\nserve large populations of ‘average’ students. These materials include content
\ndesigned for a wide gamut of different abilities. However, experts, such as <\/a>Sir Ken Robinson<\/a>, point out that the
\n‘conveyor belt’ model of education doesn’t take into account the individual
\nabilities and needs of students. Therefore,
\nwe have seen AI being used to revolutionize the way curricula is created and
\ndelivered. It can be used to provide more personalized curricula or personal
\nproblem sets for students. Instead of every student working through the same
\nset of problems or questions, they receive a set that are customized to their
\nspecific level.<\/p>\n
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\nin mathematics, but have a problem with trigonometry. Instead of putting the
\nstudent through the standard curriculum, he or she would spend less time on
\nfractions and more time on trigonometry. As a student proceeds through a
\ncourse, AI will monitor his progress and self-modify to meet the specific needs
\nof that student.<\/p>\n
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\nplagiarism has become a huge issue. While detecting plagiarism is quite easy –
\nthere is already AI that can detect direct copying and similar text where just
\na few words or the tense are altered — there are other challenges. For example,
\na student may take content from one language and translate it to another. This
\nis harder to detect, but AI is being developed to solve this problem. Similarly, image interpretation AI
\nis being developed to find instances where arts students copy or imitate a
\ndesign.<\/p>\n
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\nFactories<\/b><\/p>\n
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\nHowever, they differ from many other AI applications as they don’t focus on
\nend-users. Instead, they focus on products, produce and machines. This requires
\nan investment in sensors, robots and automation, and the optimization of
\noperations.<\/p>\n
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\nthe generalization of findings between different areas. For example, if AI is
\nbeing used to increase yields in an apple crop, can those AI models be
\nreapplied for the growing of other fruits such as bananas or peaches? Similarly, if a factory is
\nmanufacturing LCD panels and has found ways to increase their yield rates, can
\nthose tools and lessons be applied to other manufacturing processes and
\nfactories?<\/p>\n
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\nabout AI in 2021 and beyond can be summarized in one word: leverage. Using
\nexisting AI model architecture, combining well developed models and finding
\nways to generalize existing models to other applications will continuously
\nincrease the impact of AI along with accelerated digital transformation across
\nmany domains. For more artificial intelligence and machine learning blog
\ninformation, please refer to the Appier blog<\/a>.<\/p>\n
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