Digital transformation has made its way to analytical science.

Analytical science is a broad field impacting industries across the globe. One area where analytical science is seeing a surge is biotherapeutics which demands working with complex molecules that are challenging to analyze. As a solution to address the pressing issues, automation in analytical science is the need of the hour.

Biotherapeutics works with large and complex molecules. While the large surface area of these molecules results in a high rate of interaction with the drug targets, this large size also interferes with characterization techniques. The main characteristics researchers look for in biotherapeutics are protein glycosylation analysis, protein aggregation analysis, host cell protein analysis, and biomolecular interaction analysis. Researchers use an array of techniques like hydrophilic interaction liquid chromatography, two-dimensional liquid chromatography, bio-layer interferometry, and mass spectrometry techniques. This where automation is critical. Through these techniques, the knowledge of product consistency, safety, and efficacy are communicated throughout the processes. Automation will ensure consistency in data which is the crux of in-depth characterization.

The COVID-19 pandemic has put a spotlight on the need to automate almost every significant industry, including analytical sciences. During the initial phase of the pandemic, laboratories across the world had to maintain a minimum number of people to be productive while following social distancing laws. As laboratory staff has limited time to monitor the processes, automation will reduce the workload.

The thought of automation is not new to this industry. Partially automated systems, that require human intervention, were used for specific applications. That stemmed the requirement for complete automation that could facilitate improvements to the practices like data reproducibility, data tracking, and optimizing human efforts.

Once there’s a unanimous vote on automation, the next big hindrance is to successfully incorporate it into existing workflows. Researches need to strategize the alterations in the current workflow to make space for the new technology. Organizations should also be prepared for budgetary revisions and training programs. While today’s automation is simple and hands-free, some cases might require instrumental knowledge. Hence, automation will become a gradual process in the laboratories.

Owing to technological advancements, laboratory automation also witnessing advanced instruments and software. The recent pandemic only leveraged the adoption of automation and boosted digital transformation. A range of new solutions like Thermo Scientific Vanquish UHPLC Loader and Termo Scientific inSPIRE Collaborative Laboratory Automation Platform, by Thermo Fisher Scientific, are adding sophistication to upstream sample preparation, downstream analysis, and scientific workflows with no human interference.

It’s time for analytical sciences to transform with automation, especially the large workflows related to biotherapeutics. For end-to-end development, automation tools need to be adopted rightly to make the effort worth it. Like any new technology, combining it with a traditional workflow will be challenging and a time-consuming process, but this will enable laboratories to start operating with minimal downtime.

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Over the last decade, data science has been rapidly progressing both as a technology and as a discipline. Best practices have been created by the leading businesses and it is now becoming part of the operational core for organizations. However, there is a need for a next step for product evolution in data science platform that supports and provides both business users an integrated solution for managing, building, and optimizing predictive models.

Nowadays, data science platform is the most talked about topic in data science meet-ups, conferences, and top publications. According to a Research Dive analyst review, the concept of data science platform is not novel in the big data space but the need of data science platform in business is still unknown to many.

Need for a Data Science Platform

 1)    To Enable Better Teamwork with Data Scientists

If the data scientists are solving the same problem in several ways, the productivity will decrease as it won’t deliver effectual value to the organization. One of the best solutions to ensure effective teamwork with data scientists is to provide them with a centralized flexible platform and the required set of tools to work upon. By using a data science platform, it ensures that all the contributions of the data scientists i.e. data models, data visualizations, and code libraries exist in a single shared reachable location. This helps data scientists to reuse the code, facilitate better discussion around research projects, and share best practices to make data science easily scalable and less resource exhaustive.

2)    Help Minimalize Engineering Effort

With data science platforms, the data scientists get help in moving analytical models into production without any need of additional engineering effort or DevOps. For instance, if a company wants to build a product recommendation engine then the data scientist will require the efforts of a software engineer for testing, refining and integrating the data model before the users start seeing the product recommendations on the basis of their behavior. A data science platform makes sure that the data models are accessible behind an API so that the data scientists do not have to depend much on engineering efforts.

3)    Help to Offload a Number of Low Value Tasks

The burden of data scientists is released with the help of data science platforms. The burden of low value tasks such as reproducing past results, configuring environments for non-technical users, running reports, and scheduling jobs is offloaded from data scientists.

4)    Facilitate Faster Research and Experimentation

Data scientists do not have to deal with extra data management tasks, as data science platforms allow people to see what and how others are working on. Moreover, whenever there is a new hire in the data science team, the employee can quickly start working as it is easier to restore the work of the people who leave through a unified platform over various isolated tools.

The Market Overview

Currently, the global market for data science platform is progressing rapidly and is about to positively grow in the near future. According to the Research Dive report, the global data science platform market is projected to garner a revenue of $224.3 billion at a 31.1% CAGR from 2019 to 2026. This is majorly due to the growing adoption of analytical tools across the globe for learning the unobserved customer purchasing pattern. The key prominent players of the market are adopting several strategies such as product development along with many approaches such as collaborations and R&D activities to stand strong in the global market. The major players of the global data science market include Alphabet Inc. (Google), Databricks, Domino Data Lab, Inc., Civis Analytics, Dataiku, Cloudera, Inc., IBM Corporation, Anaconda, Inc., Microsoft Corporation., and Altair Engineering, Inc.

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Imagine if funding and donating to an NGO or a cause anywhere in the world was as easy as ordering on Amazon. With today’s technology and integration of information, and a little bit of work filling in the gaps, this is doable.

Philanthropy has seen many efforts through initiatives such as, for example, the Giving Pledge, a commitment by ultra-high-net-worth individuals to donate the majority of their wealth, to reach donors and expand giving. While these efforts are essential, more is needed to reach smaller classes of donor and to help them connect with credible causes that match their interests easily.

With the Covid-19 crisis revealing deep structural and social inequalities across the globe, philanthropy and philanthropic dollars need to be optimised to help mitigate the urgent health, economic, social and fiscal impact of the pandemic. A collective humanitarian response that can make targeted giving easier for large donors while harnessing the power of smaller, informed donors is now more critical than ever.

There is a way to achieve this, through the development of a cloud-based philanthropic platform that utilises artificial intelligence to amalgamate credible data on global needs and global giving into easily accessible and comprehensible information that donors can act upon. Such a platform could allow donors to filter their searches on location, themes, organisation size and charity ratings, as well as funding gaps.

Where policy responses fall short and gaps in the allocation of urgent funding exist, an AI-centred philanthropy data platform could allow donors to respond to economic and social challenges based on reliable, real-time global data and analysis.

AI technology is already used as a tool to advance efficiency and growth in businesses. Companies like Netflix and Amazon use AI recommendations to match offers to individual preferences and purchases and to inform content development, optimising their profits.

Similar technology, albeit for the purpose of global common good could be used to ensure that supply of philanthropic donations meets the demand for support while improving transparency and accountability. AI in the form of a recommendation engine could, for example, match donor criteria from themes to locations to ratings and NGO partners with causes via the philanthropic platform.

A 2018 report on private philanthropy by the Organisation for Economic Co-operation and Development (OECD) found that only 28 per cent of funding benefitted the least developed countries. The data shows that private development financing is largely bypassing the most vulnerable. Moreover, financing often misses the actual needs of local communities, as funding is likely to be allocated without the weigh-in and participation of local charity or NGO staff. By logging onto an online platform, philanthropists and individual donors could see where giving is capped or in excess, enabling them to invest where they care while matching needs on the ground and maximising the impact of their investments.

Data and analysis made available through AI recommendations could also fill gaps in available information needed to inspire potential investors. It could also provide a feedback loop for NGOs on what they can do to improve their standing. In addition, philanthropists and individual donors could use AI recommendations to give directly to causes that they care about, reducing reliance on intermediary organisations to distribute funds. That would allow for more opportunities to directly engage with beneficiaries.

Implementing AI to optimise giving within the philanthropic sector is not a new idea. NGOs and campaigns around the world are already using it to enhance the impact of their activities. Examples include platforms like Philanthropy.ai and organisations like WaterAid, which use AI recommendations to connect donors with beneficiaries and causes around their respective work.

While these efforts demonstrate the benefits of AI for specific philanthropic activities, a cross-sector, collaborative approach is needed to fully harness the benefits of AI beyond individual causes and for the sector as a whole. An AI-centred, cloud-based platform has the potential to address this need by engaging stakeholders invested in philanthropy across sectors in an unprecedented, real-time mapping of the philanthropic landscape.

Such a platform could also expand the pool of available data, and help to ensure that new philanthropic investments build on existing successful interventions by securing the buy-in and input of stakeholders across sectors. Qualitative data made available on the platform could also serve as a valuable resource for campaigns like the Giving Pledge and inter-governmental partners like the OECD, providing them with lessons to direct their interventions and greater visibility to promote their activities.

Finally, the platform itself could allow a virtual space for philanthropists, individual donors, NGOs, intergovernmental organisations and other partners to connect, exchange lessons and explore collaboration and co-funding opportunities to strategically drive investments and direct support where needed.

A joint effort and long-term commitment from a broad range of members of the philanthropic community is needed to develop a platform like this, and to provide the inputs that AI could use. It will require the leadership of a credible international organisation, the collaboration of tech companies and input from local and international NGOs and inter-governmental organisations.

Following the development phase, vetted local chambers of commerce, foundations, local NGOs and bilateral organisations would need to collect data on philanthropic support and feed the data to the AI in an organised way, such as through the use of tags.

Of course, there are drawbacks to using AI technology and challenges that should be mitigated during the development and implementation phases. Nonetheless, with the scale and complexity of humanitarian crises growing and the primary and secondary impacts of the Covid-19 pandemic looming, AI has the potential to empower philanthropists and individual donors while strengthening the world of giving to meet the challenges ahead.

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A new report from the Insurance Institute of Canada (IIC) has found that technologies involving artificial intelligence (AI) and big data analytics will play a big role in reshaping the insurance industry in the next 10 years – and insurers will have to prepare accordingly for these anticipated changes.

The report, entitled “AI and Big Data: Implications for the Insurance Industry in Canada,” noted that both AI and big data are expected to transform the industry in very big ways because of the multiple opportunities they present – particularly when it comes to supporting underwriting decisions, claims management, investment management, hiring practices, and even marketing.

According to the IIC, “AI has the potential to fully harness the power of data to better anticipate and serve the needs of consumers.” It can also be used to automate the most tedious of insurance tasks, freeing up valuable time for the insurance workforce.

Meanwhile, big data analytics refers to utilizing advanced tools to analyze complex mass storage of data. In the past, insurers could only conduct actuarial analysis of recent loss experience to anticipate future claims on a quarterly or annual basis because they were limited by their analytics capabilities. However, the IIC said that current technology allows insurers to use real-time analytics to better understand the needs of their consumers, track industry trends, and make better informed decisions on claims, pricing and operations.

To best anticipate this tech revolution, IIC stated that “a focus on insurance consumers and their perspective will be critical to facilitating wide acceptance of new tools, systems and technologies.”

Insurers should also look to other industries that have undergone rapid change in response to emerging technologies, the institute said, and adopt the lessons they learned on managing risks. They should also expect insurance regulators to take interest in how the industry utilizes AI and big data, so insurers should champion these new approaches and demonstrate how they benefit customers in order to build confidence in the insurance industry, IIC advised.

The IIC has provided recommendations for the industry to address risks in AI and big data over the next 10 years:

1. Inform consumers;
2. Embrace innovation;
3. Be prepared for uncertain regulation;
4. Create new insurance programs;
5. Take the time to do it right (and don’t be scared to fail!);
6. Respond to changing consumers;
7. Invest in new technology; and
8. Accept different views of fairness.

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Machine learning is enabling a smooth shift in this COVID-19 struck world.

Machine learning is one of the most used technologies in this generation. It has varied capabilities that can transform businesses across industries for the better. From being considered as a niche technology, machine learning is now seeing an increased adoption within companies in all sectors.

From a global perspective, brands are leveraging machine learning to accelerate innovation and better customer experience. For example, Nike uses machine learning for personalized product recommendations. In the F&B industry, Dominos maintains its 10 minutes or less pizza delivery time using machine learning technologies. Another widely used example is how automobile giant BMW uses machine learning to analyze data from vehicle subsystems and predicts the performance of vehicle components and recommends when they should be serviced.

In 2020, machine learning became a priority for tech companies in order to achieve revenue growth while reducing costs. In 2021, those companies are now exploring many matured applications of this technology. Disruptive tech organizations have been leading this technology across many areas like process automation, customer experience, and security.

Following the continuing growth trend, these five industries are likely to adopt machine learning to change their business processes in 2021.

Healthcare Industry

The coronavirus global pandemic has highlighted the importance of investing on and optimizing the healthcare systems. Machine learning is being considered as the most promising technology that enables healthcare providers to generate large volumes of data for insightful clinical decisions. Machine learning also enables huge processes in drug discovery, cutting down the long discovery and development time and reducing overall costs. It can also improve healthcare delivery systems to better the overall quality of healthcare under low costs. In the future, machine learning is predicted to be a critical part of clinical trials. Including pharmaceuticals and the biotech industry, machine learning will be having a huge impact in all aspects.

Banking and Finance Sector

The banking sector is already seeing many advanced use cases of machine learning, especially when it comes to fraud detection and automating processes. Machine learning applications will be proactively explored in areas in trading, investment modeling, risk prevention, and customer sentiment analysis. As countries are making digital transactions their primary mode of payment, machine learning is combining predictive analytics to play a pivotal role in helping financial companies to improve transaction efficiencies within the entire transaction lifecycle. Banks and financial institutions will also use machine learning technology to customize their banking products and offerings to stay up to date in the competitive environment.

Media And Entertainment Industry

Media giants like Amazon and Netflix have already popularized the data-based content consumption channels in recent times. When the world got initially struck with the global pandemic, the demand for new consumption models grew and left companies to leverage their artificial intelligence and machine learning capabilities to create value for the customers. In this process, machine learning is going to be crucial for the media and entertainment industry , whether it’s developing better recommendation engines, delivering hyper-targeted services, or presenting the most relevant content in real-time. Predictive modeling will also be key in communicating with the customers on time, anticipating their future demands, and making good investments.

Retail And Commerce Industry

The retail industry saw a big shift owing to the coronavirus pandemic. The pandemic has disrupted many traditional practices of this industry and machine learning has become a key enabler of change. From the perspective of brick and mortar stores or e-commerce companies, machine learning is helping this sector reinvent their supply chain, inventory management, predicting user behaviour, and analyzing trends. Dynamic pricing is emerging as a key machine learning application to help retailers thrive in the competitive market.

Manufacturing Industry

IoT devices have already flooded this industry and it is only going to increase. Machine learning will be critical to bridge the gaps created by huge amounts of data. It will serve as a building block for the industry along with automation, data connectivity, real-time error detection, supply chain visibility, warehousing efficiency, cost reduction, and asset tracking. Keeping traditional processes aside, machine learning will facilitate innovation and efficiency in the coming days.

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The term “digital transformation” has become so ubiquitous that it can mean almost any change from a manual process to an electronic one. But why do we have to think of change in terms of digital transformation? Few would argue that replacing an inefficient manual task with automation is a “transformation.” However, I think of change in terms of innovation, in terms of altering how we do something or the way we behave—in terms of disrupting an ecosystem. Innovation isn’t just automating processes that already exist, but rather applying technology to solve a problem in a different way.

In my view, one of the best ways organizations can approach a given problem space is by leveraging the myriad of data they collect every day. Data analytics comes to mind, of course — crunching a sea of data to find correlations and insights we can use to make a process better. How then do we decide what to do with those insights? You develop and train machine learning (ML) models to make more accurate, unbiased decisions based on the available data. Then you apply artificial intelligence (AI) to suggest the best way to act on those decisions to improve the chance of a successful outcome.

Using AI/ML for innovating the customer experience

One of the most visible targets of transformation initiatives is to improve the customer experience. The internet has removed geographical distance as a barrier between you and your competitors, so a company’s online presence is more important than ever. That’s why everyone is rushing to provide ever more-engaging online experiences, to hold a prospective customer’s interest.

Numerous companies provide website plugins to track a visitor’s clicks and actions, analyze them to intuit intention, and determine, for example, what content, advertisement, or offer to display next. Going beyond that, today’s most successful e-commerce sites also use AI/ML to personalize each shopper’s experience, like the order and presentation that will most likely result in another click or a purchase.

AI can anticipate with near certainty—based on past and present action, search patterns, profiles, external demographics and more—what a customer wants to see now and will do next. If successful, your website visitors will come to feel at-home, excited, and perhaps even brand loyalists. They’ll buy more and return more often.

But digital innovation shouldn’t stop with customer experience

There is nothing wrong with applying analytics, AI, and ML to create a more innovative and engaging customer experience. Not doing so can put you behind your competition. It’s all about building customer loyalty and boosting revenue.  

No matter how important customer experience is, however, it is a mistake to believe it is the only operational area that can (and should) be transformed using technologies like these. After all, today’s enterprise amasses data about more than just customers and orders. Your company, product, and delivery must broadly innovate — and all these happen on the backend. The efficiency of your internal operations — your support team, supply chain, production, inventory, quality control, human resources, and so on — can all benefit from applying AI and ML technologies. Consider just a few of many possible examples.

• Motivating a remote workforce – With so many teams working remotely, first-hand observation of employee engagement is next to impossible today. AI can analyze which applications employees use most, possibly even judging their levels of efficiency or frustration. Organizations can understand how happy, motivated, and engaged teams are so they can maintain or increase efficiency and productivity.
• Refining a business model and marketing – Beyond mere numbers, AI can analyze which products in your online portfolio work best and for which shoppers. Yes, this can help you shape the online customer experience. But it also lets you adapt which products you choose to keep or eliminate from your lineup (your business model) and adapt your offers based on observed customers’ choices or preferences (your marketing strategy).
• Protecting intellectual property – Organizations can even protect their patents, intellectual property, and product uniqueness by using AI, ML rules, and image recognition to smartly crawl the web to identify look-alike products and would-be theft.

The possibilities for internal process improvement across the enterprise are endless.

AI/ML isn’t just for large technology companies

In short, companies should apply AI/ML innovation to their operational processes as much as they do to the customer experience. Artificial intelligence isn’t just for technology companies, nor is it for analyzing and solving only technology problems. Organizations can use it to better understand their customers. They can use it to automate inefficient internal processes. They can leverage it for improving online security, boosting employee engagement, and reducing theft and risk.

Why aren’t more companies using AI? Frankly, they do not know how to start. They know they need to use it, but they don’t know where to “plug it in” to their systems first. Or they think they have to hire a team of AI engineers and build their solution from scratch.

Today AI is available for any company to use and benefit from, even smaller companies, without the need for a team of experts. There are many commercial apps and solutions in the marketplace that readily adapt to an organization’s existing processes. Some are even SaaS- and cloud-based solutions, meaning they do not require a big infrastructure investment to get started. The important thing to know is that any company can start small and scale up their AI solution in their own time — but getting started is the only way to stay competitive.

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As Information Technology (IT) complexity grows and the use of AI technologies expand, enterprises are looking to bring in the power of AI to transform how they develop, deploy and operate their IT. IBM is has recently launched a broad range of new AI-powered capabilities and services to help CIOs automate various aspects of IT development, infrastructure and operations, including:

• IBM Watson AIOps: leverages AI to reliably operate enterprise applications and automate the detection, diagnosis and response to IT anomalies in real time.

• Accelerator for Application Modernization with AI: a suite of tools within the Cloud Modernization Service designed to reduce the overall effort and costs associated with application modernization through advanced AI technology from IBM Research. This accelerator leverages continuous learning and interpretable AI models to adapt to a client’s preferred software engineering practices and stays up-to-date with the evolution of technology and platforms.

As is the case with much of IBM’s AI development, as per the company’s blog, significant portions of the technologies underlying Watson AIOps and the Accelerator were born out of IBM Research. This new offering and service — part of what the company is calling AI for IT — is the culmination of years of research and development at IBM Research into how AI can be used to transform the IT lifecycle.

Watson AIOps

CIOs and their application Site Reliability Engineering (SRE) teams are overwhelmed by the sheer number of tools for operations with data fragmentation across them and the complexity of issues, making IT operations a challenging domain. Watson AIOps reimagines IT operations with AI by enabling the prediction of problems and offering the potential of proactively fixing them. Breakthrough AI techniques developed by IBM Research enable Watson AIOps to discover new patterns in IT operations, remove noise, correlate problems across multiple data sources and make recommendations to fix them.

More specifically, the innovative tools and techniques in Watson AIOps correlate various multi-modal signals in IT operations by leveraging structured, semi-structured and unstructured data sources for building correlation models. For example, when an enterprise application experiences a problem, many monitoring tools start emitting a swarm of alerts, sometimes numbering in the thousands. Often, the root cause of these incidents lies in a different spot than the alerts. In fact, many times the swarm of these alerts distract SRE teams, wasting precious time during which the application continues to suffer operational instability or, even worse, failure.

SRE teams typically evaluate a multitude of data sources including metrics signals, application and system logs, the swarm of alert signals, and even past incident tickets. Many existing tools in IT operations can individually build models in each of these data silos to predict continuous values of the input signals or predict probabilistic class labels. But, they still treat the input spaces in isolation, resulting in hundreds of anomalies and, again, overwhelming the support teams. This is precisely where AI technology behind Watson AIOps steps in. It correlates among the diverse data sources to localize the real root cause, create an explainable diagnosis and recommend the best course of action.

To do the above correlation, the algorithms need to work with time series data of metrics, semi-structured — but voluminous— data logs, structured data like alerts, and unstructured data in incidents and human conversations to automatically create a timeline of the evolving issue. Each of these data sources better lends itself to certain types of tasks. Time series data, for example, is more suitable for regression tasks, whereas unstructured data is best for classification tasks. Logs and other semi-structured data can be used for either of the tasks after suitable transformations. For SRE teams to resolve the incident impacting the enterprise application, they must first uncover patterns across these diverse, multi-modal signals in order to reduce the problem from thousands of alerts to hundreds of anomalies coalesced into a few possible evolving stories.

Watson AIOps also leverages semantic search techniques that can relate the current incident to past incidents, analyzingthose contextual cases, and suggesting possible next best remediations. IBM AI innovations, like Watson OpenScale, are at the forefront of developing trusted and explainable technologies, and we leveraged those innovations tohelp SREs interpret the reason behind a Watson AIOps recommendation which is critical to trusting those actions.

Accelerator for Application Modernization with AI

A major hurdle CIOs face in leveraging the cloud paradigm with core business applications is that these applications were written for an on-premise world and not architected to leverage cloud-native architecture and modern DevSecOps principles. Application modernization is about optimizing an enterprise’s application portfolio and transforming it to meet the rapidly evolving needs of business agility and competitiveness, all while leveraging these new programming models and cloud architecture.

Many large enterprises have thousands of legacy application that must be moved to the cloud. As enterprises move these mission-critical workloads, they face difficult decisions about containerization. CIO teams making these decisions must consider, for example, application criticality, behavior, operational requirements and hosting infrastructure. This decision-making process is largely manual and often error-prone when done at scale.

The Accelerator suite consists of three tools in which IBM Research led development:

Application Containerization Advisor (ACA): This asset uses AI to quickly provide more confident recommendations for containerization. ACA applies extensive and evolving knowledge graphs to help infer missing data and compute matches to the possible container references. The AI-enabled advisor also employs continuous learning models to provide highly accurate containerization recommendations that improve over time. The tool also considers 12-factor properties to provide insights into the complexity of containerization activity, and its AI Explainability features help IT departments understand assessments of feasibility, complexity and risk.

Candidate Microservices Advisor (CMA): Legacy applications typically bundle functionality across multiple business and data domains into a single deployable application, i.e., they are implemented as a monolith. This severely restricts a business’s ability to roll out frequent feature releases that may impact only some pieces of functionality or domains. It is also difficult to scale the performance of certain functionalities up and down selectively and dynamically, as needed by changing usage load levels. CMA automates the discovery and design of candidate microservices from the source code and data artifacts of a monolithic application. It also captures application details including component entities, for example, Java classes and database tables, as well as the relationships between these entities and the transactions contained within the application. The idea is to determine whether and how the application can be broken down into microservices that can be moved to a hybrid cloud environment.

Modernization Workflow Orchestrator (MWO): This functions as an AI-driven system to introduce applicationmodernization at scale through standardized tools and architectural best practices. Think of Modernization Workflow Orchestrator (MWO) as a GPS for modernization. MWO employs AI planning technology based on symbolic reasoning to dynamically create modernization steps tailored to each application. The tool also leverages Natural Language Processing (NLP) and Machine Learning (ML) to accelerate the capture of knowledge about modernization actions for the AI planner.

Continued AI Innovation for the Enterprise

The availability of Watson AIOps and Accelerator for Application Modernization with AI is another example of IBM Research helping the company move quickly from innovation to product. IBM Research has also helped develop many of the NLP capabilities driving Watson Discovery for document understanding and Watson Assistant for virtual agents. This includes our announcement in March that IBM will begin integrating NLP features from IBM’s Project Debater into Watson, ultimately enabling organizations using Watson Discovery, Watson Assistant and Watson Core Services to take advantage of advanced sentiment analysis, new summarization capabilities, advanced topic clustering and customizable classification of elements in business documents.

Looking Ahead

IBM Research will continue developing ways to leverage AI to transform information technology, to accelerate the process of understanding what an organization’s application portfolio looks like and help them decide how to modernize it and deploy in the cloud.

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We can say that we have reached exciting points in the images that can be created by artificial intelligence. New techniques developed in research laboratories provide more successful results in this field. A new AI algorithm that turns human faces into animals is evidence of this.

Educator and artificial intelligence researcher Xander Steenbrugge has developed an artificial intelligence algorithm that can turn the human face into animal face and then turn it back into animal form. It can be said that the developed algorithm has made transformations quite successfully (although not as successful as in science fiction movies …). This experiment is part of the Neural Synesthesia project, where Steenbrugge gained audiovisual experiences using algorithms and machine learning models.

Steenbrugge’s work was done using productive contentious networks (GAN) that “learn” from a dataset like many image transfer studies and then try to convert the target image into the source you feed. For example; deepfake videos are also made using GANs.

Steenbrugge uses a new set of artificial intelligence production model StarGAN v2 and 15,000 HD animal photos for the change called “humanimals”. It uses this data set together with human faces as educational data and passes it through a different model, StyleGAN v2. You can watch the fantastic results that come out here.

About the new algorithm, the researcher said, “I believe that creativity has become an interactive process between man and machine, and we witnessed the beginning of a new era in digital environment. commented.

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VentureBeat’s AI event of the year for enterprise decision-makers, Transform 2020, is shifting to an online-only event to protect our community amid concerns around the coronavirus.

Transform 2020 will be held online July 15-17, expanding to three days from two. Each day is dedicated to a big theme in AI, including the Conversational AI Summit, and the Technology & Automation Summit. The third day will focus on IoT & AI at the Edge, and Computer Vision.

VentureBeat is providing a way for the AI business community to experience virtually the same engaging experiences of in-person events. For example, we’re adding digital equivalents for networking, one-to-one meetings, match-making, and live Q&A with speakers.

VentureBeat is the leading publisher of AI content coverage, and brings its AI expertise, reach and network to bear at Transform. VB’s AI channel receives 20 million page views a year, and has a social reach of 1.5 million. News coverage from Transform alone generates more than a million page views. Transform gathers together 750 executives who are director and above to discuss the best practices in applied enterprise AI. Transform also offers invite-only experiences for the VP level-and-above executives who come every year.

The event features keynote speakers from companies like Google, Intuit, Walmart, Pinterest, Salesforce, Adobe, AWS, Chase, Goldman Sachs, PayPal, Doordash, Visa, and Disney — plus from a growing list of sponsors, including Intel, Dataiku, Capital One, Booz Allen Hamilton, and Two Hat Security. Other speakers include leading AI professors from academia, including Stanford and Berkeley. Transform will also host its popular Women-in-AI breakfast, sponsored by Intel and Capital One. Transform will include a focus on diversity and inclusion topics, reflecting how ethics has become an important part of smart AI practice, and host its 2nd annual AI Innovation Awards.

VentureBeat’s virtual event will allow AI innovators and leading executives from around the world to partake despite ongoing challenges surrounding the outbreak. We expect the digital options we’re planning may allow even more people to participate than had it been offline — expanding the experience to an even wider group of businesses and executives.

As part of our digital offering, attendees will be able to view live sessions with AI leaders, join networking sessions, engage in digital roundtables, take part in our AI expo and Tech Showcase online, and participate in 1:1 meetings with world-class AI providers.

“With Transform being the only significant digital AI event for business executives on the map for 2020, it’s important we take leadership in the community, and support dialogue and networking for decision-making around AI,” said Matt Marshall, founder and CEO of VentureBeat. “We’ve created a virtual event platform for networking and 1:1 meetings to happen — so that we can bring thought leaders in AI together.”

Interested investors, vendors, and business executives can register here to join the event online. For companies interested in partnerships, we have innovative and high impact custom solutions in place for all the components for our strategic sponsors.

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Back in 2008, theoretical physicist Stephen Hawking used a speech synthesizer program on an Apple II computer to “talk.” He had to use hand controls to work the system, which became problematic as his case of Lou Gehrig’s disease progressed. When he upgraded to a new device, called a “cheek switch,” it detected when Hawking tensed the muscle in his cheek, helping him speak, write emails, or surf the Web.

Now, neuroscientists at the University of California, San Francisco have come up with a far more advanced technology—an artificial intelligence program that can turn thoughts into text. In time, it has the potential to help millions of people with speech disabilities communicate with ease.

“We exploit the conceptual similarity of the task of decoding speech from neural activity to the task of machine translation; that is, the algorithmic translation of text from one language to another,” the scientists wrote in a new paper published in the scientific journal Nature Neuroscience.

They’ve taken an AI approach that is akin to translating text in different languages. The underlying theory is the same in both cases—the goal is to convert one sequence of some arbitrary length into another—but the inputs are different, neural signals in the brain versus text.

To test out their hypothesis, the researchers used human trials. The scientists implanted electrodes into the brains of four participants with epilepsy to monitor their speech. Each person then read sentences aloud from one of two datasets: a set of picture descriptions, composed of 30 sentences and 125 unique words, which contained 460 sentences and about 1,800 unique words.

Each participant read 50 sentences aloud multiple times, including lines like “Tina Turner is a pop singer” and “there is chaos in the kitchen.” As each person spoke, the researchers monitored their brain activity. Then, they input the data into a machine learning algorithm that could switch the brain waves into a string of numbers that encoded the sentences. In another portion of the system, the numbers were converted back into a sequence of words.

At the outset, the system came up with some nonsensical phrases, like “the spinach was a famous singer;” lines with improper grammar, like “several adults the kids was eaten by;” and some ultimately philosophical-sounding sentences, such as “the oasis was a mirage.” Over time, the system improved as the researchers fed the system the initial sentences that the participants read aloud, to compare against.

In one case, the system got 97 percent of the sentences correct, representing less errors than the average human transcriber. Still, the algorithm is only processing a small number of sentences and words compared to what a user would ultimately desire.

Still, the system currently only works on verbal speech—meaning those who suffer from speech disorders caused by muscle paralysis won’t benefit just yet.

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