2023 WILL BE A TRANSFORMATIONAL YEAR FOR THE FIELD OF ARTIFICIAL INTELLIGENCE: UNLOCKING THE INDUSTRIAL POTENTIAL OF ROBOTICS AND AUTOMATION
2023 WILL BE A HUGE YEAR FOR THE FIELD OF ARTIFICIAL INTELLIGENCE: UNLOCKING THE INDUSTRIAL POTENTIAL OF ROBOTICS AND AUTOMATION
What is AI Software?
Artificial Intelligence (AI) Software is a computer program that mimics human behavior by learning various data patterns and insights. The top features of AI software include Machine Learning,?Speech & Voice Recognition, Virtual Assistants, etc. AI combined with Machine learning is used to provide users with the required functionality and make the business process a much simpler one. AI software is used to build and develop an intelligent application from scratch with the help of?Machine learning and deep learning?capabilities.
There are four different types:
?1.???Artificial Intelligence Platforms:?This will provide the platform for developing an application from scratch. Many built-in algorithms are provided in this. Drag and drop facility makes it easy to use.
2.???Chatbots:?This software will give the effect that a human or person is doing in a conversation.
3.???Deep Learning Software:?It includes speech recognition, image recognition, etc.
4.???Machine Learning Software:?Machine learning is the technique that will make the computer learn through data.
With the help of AI, we can develop smart systems that will not only help in businesses or offices but also at home. Smart systems can perform so many tasks for us, right from setting the alarm to switching on/off the lights. Gathering or collecting data from different portals becomes much easier. With the help of ML, we can apply different algorithms to data to get it in our required form. While doing online shopping, we get recommendations based on what we see or purchase. This, in turn, will help in getting more business. All this is possible, just because of AI (Deep Learning and Machine Learning). When we want to buy some products or services, we likely visit the concerned website, where we get help through online conversation or chatting window that is always available. This 24*7 help is possible only because of AI (Chatbot). RPA software copies human actions and AI copies or imitates human intelligence. AI is learning and thinking about the ability of an application.
?MCKINSEY GLOBAL INDUSTRIAL ROBOTICS SURVEY IN 2022
After McKinsey Global Industrial Robotics Survey in 2022 illustrated, that industrial companies are set to invest billions of$ heavily in robotics and automation. However, it is impossible to sustain their existence. Across the industrial world,?companies are having a big bet on robotics and automation. For many companies, automated systems will account for 25 percent of capital spending over the next five years.?Industrial-company executives are eagerly watching the benefits they reap in output quality, efficiency, and uptime. However, still many derecognize this danger, as for them the cost of hardware, and a lack of internal experience are their prime areas of concern. After a survey of some of the industrial sectors, the biggest investment in automation over the next five years is set to be retail and consumer goods, with almost one-fourth of companies from that sector already planning to invest more than $500 million. That compares with 15 percent in food and beverage and 8 percent in automotive. For logistics and fulfillment players, automation will epitomize 30?percent or above of their capital spending in the next five years—the highest share among industrial segments. With billions of dollars expected to be spent on automation over the coming years, industrial companies necessity to assure that they achieve perfect implementation as much as possible. ?Many are terrified of the idea and this state of affairs offers a market-winning set of circumstances that makes it possible to achieve grand success for technology providers. The most successful providers will be the ones that can help industrial companies succeed in dealing with difficult challenges, including those related to technology selection, execution planning, and acquisition of a set of a relevant set of knowledge, abilities, and experience needed to perform a job skill for a systematic introduction on a larger scale basis. Some aspects of productive activity are more easily controlled by automation than others are, with routine tasks at a performance limit. Operations such as picking, packing, sorting, movement from point to point, and quality assurance are by now automated to some extent, and these will attract regular heavy investment over the coming years.
?On the contrary, activities such as assembly, stamping, surface treatment, and welding, all of which require high levels of human engagement, are less likely to be automated in the short to medium term. Where operations can be automated, the benefits include making processes rapid, higher capacity utilization, and to maintain or improving high quality. Besides, there are upsides in cost, operational uptime, and safety. Simultaneously, environmental and sustainability factors are likely to be less positively affected. An influential message is that automation is difficult. Participants report that the primary challenges to adoption include the capital cost of robots and a company’s general lack of experience with automation, cited by 71 percent and 61 percent of respondents, respectively. Some say that business confidence in technology is low, leading to challenges around conviction and funding. Moreover, respondents’ expectations of production and reliability gains through automation are offset by the belief that such gains will abolish jobs and may affect existing contracts. But in reality, this concern is not true in the case since automation typically leads to changes in workplace roles rather than the creation of redundancies.
Many companies currently operate a jumble of traditional technologies and anticipate challenges in implementing a single set of solutions backed by integrated and interoperable programming and platforms for robotics and automation. Almost half of the respondents, mention challenges with finding holistic, end-to-end solution providers across geographies for the scope of robotics technologies of interest to them. Companies are responding to such issues with increased partnerships across legacy-system integrators and robotics start-ups and disruptors that offer cutting-edge innovations. Survey participants are also apprehensive in befitting robotics into existing spaces and the potential inability of machines to interface with products. Fears about safety and cyberattacks also feature as potential areas of danger. Despite significant capital commitments, many companies are besieged to translate their intentions for robotics and automation into actions, with challenges related to knowledge and return on investment is particularly difficult hurdles. In retail and consumer goods, approx. 60?percent of respondents refer to those two factors as barriers to progress. Other headwinds include a lack of technological readiness from a capability lens and system reliability. The challenges that industries face present robotics and automation providers with a significant opportunity to help build the capabilities required to automate at scale or provide support for this effort. However, those vendors will need to provide resounding answers to the questions raised by their customers and work hard to differentiate themselves from their competitors. Customers would most like to see differentiation in price, scale, and innovation. Other salient buying factors include the quality of the product build, the ability to offer an integrated solution, and the provision of reference cases that show successful rollouts. Moreover, vendors should be able to offer solutions that are cost-efficient, rapidly deployed, reliable, safe, and scalable. And they need to be able to move swiftly from prototype to scale within the customer’s organization.
THE DIGITAL REVOLUTION IS BREWING IN THE INDUSTRIALS SECTOR
One reliable way of distinction is to enhance software and hardware sales with full-service models. Such models would offer not only installation and integration but also maintenance and support through the product life cycle. Indeed, the majority of them agree that most customers favor robotics and automation providers that can provide full-service models for implementation. Automation providers that can move toward robotics as a service and act as a single point of contact for maintenance (for both hardware and software) will create a distinctive competitive advantage. Also taken into consideration is the value of variable cost models, such as cost per pick. Drilling down into maintenance and service models, above half of the industrial players would prefer that a system integrator act as a single point of contact and provide both hardware and software maintenance. Meanwhile, the same players desire a convertible model in which system integrators gradually hand over responsibilities to in-house teams. Additional common preferences are the ability to work with OEMs for hardware and integrators for software and to contract with a local third party for all maintenance needs. To offer the right service at the right time in the right way is a big ask for robotics and automation providers. However, given the need for industrial companies to augment productivity, create stable supply chains, and find an alternative to the agitated labor market, the effort is likely to be justified by the reward. The providers that effectively help companies realize benefits from robotics and automation and scale the technologies across markets are likely to emerge with strong revenues and a positive business growth path trajectory. The productivity gains and capacity flexibility arising from automation could also significantly improve operational resilience, which is critical during a time of increased disruptions
?THE FUTURE IS NOW: UNLOCKING THE PROMISE OF AI IN INDUSTRIAL
?Setting up a winning digital and advanced analytics organization is not easy. Decisions about talent and technology will often determine the transformation’s success. Digital transformations whether digitalizing an entire company or setting up a digital and advanced-analytics (DnA) start-up within the organization are a significant challenge. We have frequently seen these transformations come to an obstacle to belief along the way, and leaders often have difficulty sustaining any improvements over time. Across the transformation journey, talent and technology are critical to success, from planning and hiring to managing and developing. We recently revisited more than 30 large-scale digital transformations across a range of industry sectors, each conducted within the past three years. Our aim was to gain a greater understanding of the talent and technology decisions that drove (or hindered) the success of these programs. We have made a thorough analysis and got the emergence of five distinct core themes. We will deliberate them one by one as under:
?1. Treat as most important bringing senior digital leaders to appeal to talent and sharpen our value proposition
Ultimately, performance is defined by our talent and technology strategies and the capabilities of the senior leadership, such as lead data scientists, driving the transformation. Indeed, up to 50 percent of the variability in group or unit performance is attributable to individual leaders.?These people shape the future organization in multiple ways: screening and hiring candidates, establishing technical standards, and setting the attitude for ways of working (for example, collaborating, innovating, failing or learning quickly, and maintaining high quality). Identifying the right individuals for these roles will be key to the success of the digital transformation. Therefore, it is prime to invest the time needed to conduct a broad search, i.e., basically means we select multiple and compare them before selecting.?
Meaningfully, the right appointment also sets the tone for subsequent hires: the chief digital officer (CDO) is a key contributor to the company’s employee value proposition to attract follow-on talent. It is important to allow the CDO to hire from the top down, starting with senior roles and moving to junior roles, to enable the search for the right lead data scientist, data engineer, software engineer, and technical architect. The CDO’s experience and credibility will help convince top-tier talent to join the company after taking all other elements of the organization into consideration—such as whether the brand and story make sense, whether there is adequate compensation, and how the company addresses lifestyle issues. All of these factors are critical to effective change management. The quality of reports to the CDO will play a crucial role in achieving the initial wins needed to gain traction, such as successfully developing digital products or setting up technical infrastructure. In general, organizations risk the overall reputation and viability of their programs if they attempt to take shortcuts with early hiring. In fact, our experience shows that taking shortcuts can delay transformations by six months to a year or more. Thus, organizations should go further and be proactive in setting up the digital leader for success—ensuring that the CDO has influence and a seat at the table and that the program is large enough to require commitment and conviction from the C-suite.
2. Reconsideration our value proposal for digital talent
Although the CDO is an important factor in shaping the organization’s value proposition as it relates to talent, there is only so much that a leader can do alone. It is important to consider the local hiring market and talent pool, as well as factors specific to the industry sector, and strive to improve our own work environment in a local context. There is no need to assume that we will always be competing for digital talent with big tech companies, such as Google and Amazon. In fact, companies tend to compete with other local companies to attract key technical roles. For instance, a mining company may be in a remote area where big tech doesn’t operate, so the competition for digital talent would be primarily with other mining businesses and oil and gas companies. That said, digital skills are not industry-specific, which means that local players from other sectors, such as financial services, may also compete for the same digital talent.
After meeting minimum requirements such as salary or respected tech leadership, consider how our industry can appeal to each individual candidate’s specific needs. To stand out, companies need to be committed to a modern technology stack. Understand the factors that motivate particular categories of candidates and adjust your pitch—and work environment—accordingly. It is helpful to recognize that some industries can readily offer what a candidate may be looking for. For example, in the automotive, manufacturing, or energy sectors, there may be opportunities to address challenging or novel problems, such as the transition to a net-zero economy, in addition to working with cutting-edge technology. We may be able to offer development opportunities, including top-tier training programs or access to educational conferences. At one mining player, the company value proposition was revised to focus on what matters to digital talent; at an airline player, the high levels of data available and solutions at stake were key components of an attractive value proposition for top talent. That said, companies still have roadblocks to surmount when seeking to attract digital talent, such as remote work locations—but these are solvable.
Beyond this, it is important to develop a precise picture of our tech culture: a mix of skills, mindsets, and work preferences is necessary to build a successful organization and is preferable even within a given technical role. In this context, employers should hire for specific profiles as part of the overall organizational mix, and a clear definition of expectations aligned to different profiles can minimize confusion. Historically, culture has been the number-one barrier to delivering impact from digital initiatives.?Organizations need to understand where they are today, set the vision across both strategy and culture, and hire employees based on gaps and culture fit. Finally, team dynamics within the cultural landscape are important: employers should pay close attention to their collaboration models, carefully considering the importance some employees attach to working with groups outside of their own specialties, such as data engineers working with the business function. Based on the business problems that need to be solved and the governance model that will steer the work, it is important to understand what individuals experience on daily basis and to appeal to them accordingly. Peer groups and the expected level of responsibility are equally important. Make sure we are accurately plunging the level of a given team. A colleague who signs up for a “tier one” team and finds that they are working on less supercilious problems may lose interest and look for an exit.
3. Hire digital talent internally, but keep a high bar on technical skills, and be realistic about reskilling
Not all digital talent comes from outside the organization. In fact, companies often have untapped pockets of digital talent. To begin, appraisal—and hiring—processes for technical roles should include technical-competency assessments rather than just résumé reviews and evaluations of leadership skills. At the same time, not all digital products require sophisticated skill sets. Companies with strong nondigital talent can aspire to cover up to 70 percent of their digital needs by upskilling some of their current employees. Being able to spot these people is vital and can be achieved via techniques such as skill surveys. A decision to train an existing employee versus hire externally needs to be based on measurable criteria, such as the time for a candidate to become fully independent in a role. It is important to be realistic about both the number of employees who can be upskilled and the time required to undertake the training and development journey. The first employees to upskill would be those with high data and technical readiness and who benefit from strong business sponsorship.
Beyond this, companies should place internal hires in positions where they can learn and grow while working alongside more experienced engineers, whether these are hired externally or staffed via a third party. In addition, a sometimes-overlooked benefit of hiring from within is that internal hires can strengthen the link between development or product teams and operations. Executives tend to overstate how quickly their existing talent can be converted. If we decide to upskill internally, we should consider the speed with which we are trying to deliver on use cases. Retraining people within IT is not easy, and some roles are too specialized for reskilling (such as cybersecurity engineers and system architects). In such cases, it’s better to hire specifically for that role from the marketplace. For example, it can take up to or more than 100 hours of weekend work for an internal employee to complete the online coursework to pass an Azure data engineer certification. That approach won’t fill our teams very quickly. If we choose to do most of it ourselves, it makes sense to convert internal talent, but don’t imagine that a process engineer can be converted with two months of training. As an example, it can take years to become a high-quality data scientist or technical architect. Unless we hire highly proficient people initially, we are unlikely to obtain the early wins our program needs to gain traction. We even risk the overall reputation and viability of the program if we try shortcuts with early hiring.
4. Build a learning and development program specifically for digital talent
Skills development needs to extend beyond training because the sheer pace of technological change can make setting up formal training programs difficult.?A blend of on-the-job training and structured learning programs to round out skill sets can best foster the development and embedding of DnA skills. Accordingly, ?an apprenticeship model can work well, which is why it’s important to hire senior leaders first. When matched with more junior employees, who are often eager to work with senior employees, flagship hires and expert temporary contractors can provide powerful on-the-job learning. This push toward ongoing learning also applies to senior employees, including executives and senior technical leadership. Ideally, they should spend half to two-thirds of their time actively doing day-to-day work. This way, everyone is involved in developing the final product, improving both upskilling and retention. Notably, many successful organizations focus on creating the types of environments in which workers can teach themselves. For example, at Google, the vast majority of tracked training happens via an employee-to-employee network called “g2g” (Googler-to-Googler). Members of the network, which includes more than 6,000 people, offer their time to help peers develop. Leading companies are much more inclined than laggards to reward higher skill levels with better compensation, greater benefits, and more responsibility. Employees realize that they must upgrade their skills continually, and there are numerous ways to do so—especially online, where free or affordable courses are available for certification in high-demand technical skills such as machine learning, Python, or R, which they can take at work or during personal time. Of course, structured learning helps round out new skill sets and fosters a longer-term learning journey. It is essential that cohort- and role-specific learning journeys are in place across the enterprise from the top all the way down. Learning journeys for different cohorts—for example, the chief experience officer’s team, data engineers, translators, and those operating the products being developed—include components of online courses as well as in-person cases to achieve a mix of self-paced (fundamentals) and group (interactive) learning. Finally, it is important to be realistic. Best-in-class data scientists spend many years in school and then more years in a working role before being hired by leading firms. It is not possible to re-create this with an internal six-month training program.
5. Evaluate trade-offs between immediate results and long-term capability building, leveraging temporary contractors to supplement delivery
Embedding new skills and culture is vital for the success of any transformation. However, there are trade-offs to be considered between quick wins and sustainability. ?All companies need these skills; only how much and when. Contractors can help expedite the early pace of the transformation, but a strong transition plan must ensure successful capability transfer and ownership. A number of actions can help achieve this:
In general, balancing speed, sustainability, and degree of innovation requires trade-offs. Successful iteration necessitates a culture that can fail fast and learn from those failures. Without this, it’s important to ensure that plans work out in the long term. Hiring strong senior leadership will serve as a catalyst for growth. ?Prudently, constructed value propositions, key performance indicators, and career pathways will attract, motivate, grow, and retain talent. A crucial scale-up consideration is to regularly revisit key trade-off decisions, such as achieving the right blend of training for our employees and the optimal mix of talent sources to balance culture, pace, and quality. Thinking about these themes and iterating on them will help our DnA organization grow to its full potential.
The Digital Factory: how to scale our digital transformation
?Coca-Cola: The people-first story of a digital transformation. Avatars in our social media feed, the prevalence of text-to-image tools, and ChatGPT’s popularity are just a few examples of how?generative AI?has recently gained public attention. It is a subset of artificial intelligence that is concerned with creating new data that is comparable to current data. Deep learning algorithms like generative adversarial networks (GANs) or variational autoencoders are frequently used for this (VAEs). The widespread increasing demand for?top Generative AI start-ups?will soon fundamentally transform how businesses function, grow, and scale. Generative AI start-ups help us in producing novel and realistic visual, textual, and animated content within minutes. According to Gartner, by 2025, the percentage of data generated by generative AI will amount to 10% of all generated data.?Generative AI Start-ups?have offered a more upgraded variety of generative AI that goes further than just monitoring a real-life environment to generate content.
SOME IMPORTANT AI FOUNDED
PAIGE.AI WAS FOUNDED IN- 2018 LOCATION- USA
Paige was launched in early 2018 to bring technology created at Memorial Sloan Kettering to the world. The company is developing novel deep learning algorithms based on the thing that is complex and difficult to follow: recurrent neural networks as well as generative AI models that are able to meet customers’ demands.
SYNTHESIS AI WAS FOUNDED IN- 2019 LOCATION- USA
Synthesis AI assembles novel generative AI models and evolving technologies from the CGI world. Large volumes of data can be produced using their proprietary pipeline to train advanced computer vision models. Their one-of-a-kind API creates millions of photos with distinctive people in various settings and environments. Simply specify the desired distributions in JSON, submit a job request, and their platform as a service scale in the cloud effortlessly to produce terabytes of data with ease. It is among the leading?Generative AI start-ups?for 2023.
VISUAL:?AI FOUNDED IN- 2020 LOCATION- FRANCE
Veesual AI is an artificial intelligence company that creates virtual try-on experiences for fashion e-commerce. The company focuses on mixing & Matching Style Experiences for fashion e-commerce with the help of Generative AI. Veesual AI is among the top?Generative AI start-ups to look for in 2023.
TOKO WAS FOUNDED IN- 2021 LOCATION- USA
Toko helps English learners in East Asia achieve speaking fluency. Through their mobile app, students have a conversation with an AI in brief, realistic chats and get grammar corrections. Learners can practice real-world scenarios including small conversations and workplace dialogues with the help of more than 150 topics. Toko provides a relaxed atmosphere to assist students to gain confidence. The business makes language proficiency available to everyone, not just those who can afford to meet one-on-one with a tutor. Toko is the third-best-rated educational app in Taiwan’s App Store.
ANDI WAS FOUNDED IN- 2021 LOCATION- THE USA
Andi is searching for the next generation. It’s a novel kind of conversational search engine with a clever AI helper that provides direct answers to difficult topics. It’s similar to messaging with a knowledgeable friend who serves as a conduit for online information. It provides you with the greatest and most helpful links while explaining and summarizing difficult topics. Additionally, it shields you from ad tech and spam. Andi functions by fusing sizable language models with real-time information, logic, and common sense. Our users describe the outcomes as “magical.”
REPHRASE.AI FOUNDED IN- 2018 LOCATION- INDIA
Rephrase.ai focuses to democratize video, making high-quality video creation capabilities available to companies of all sizes across all industries. It uses deep learning to generate digital avatars of actual humans that can be used for synthetic video content.
TAVUS WAS FOUNDED IN- 2021 LOCATION- USA
Tavus is focused on building a new video-based experience for marketing and product teams. The company makes personalized video outreach scalable with AI. Record a video once, and send it to thousands instantly completely customized to them.
HELLO COGNITION WAS FOUNDED IN- 2022 LOCATION- USA
Hello is a next-generation search engine that answers complex questions by combining information from multiple relevant sources into a single concise explanation. Hello is particularly adept at answering complex technical questions that other search engines cannot and can generate code snippets on the fly. If you are curious about a topic, then ask a follow-up question and Hello can dive deeper using the existing context of the conversation.
EVERY AI INC. WAS FOUNDED IN- 2020 LOCATION- THE USA
Founded by Computer Vision PhDs at UIUC, Revery AI creates a scalable method to visualize garments on people. The technology powers a virtual dressing room where shoppers can mix & match outfits and visualize them on any model instantly online. Their product helps retailers achieve 3.8x conversion rates and 3x add-to-cart rates.
领英推荐
ORDERS BIO FOUNDED IN- 2019 LOCATION- USA
Ordaos uses generative AI technology to accelerate the mini protein drug discovery and development process. Ordaos employs a powerful proprietary AI engine and biological expertise to allow researchers to see critical patterns and relationships and help to come out with a fruitful result.
?GENERATIVE AI START-UP: TEN AI PREDICTIONS FOR 2023
1) GPT-4 will be released in the next couple of months but be a big deal.
Buzzwords spreading recently about GPT-4, the next generation of OpenAI’s powerful generative language model. Expect GPT-4 to be released early in the new year and to represent a dramatic step-change performance improvement relative to GPT-3 and 3.5. As manic as the recent hype around ChatGPT has been, it will be a mere prelude to the public reaction when GPT-4 is released. Perhaps counterintuitively, we predict that it won’t be much larger than its predecessor GPT-3.?DeepMind researchers determined that today’s large language models are in fact larger than they should be; for optimal model performance, today’s models should have fewer parameters but train on larger datasets. Training data trumps model size.
Most of today’s leading language models were trained on data corpuses of about 300 billion tokens, including OpenAI’s GPT-3 (175 billion parameters in size), AI21 Labs’ Jurassic (178 billion parameters in size), and Microsoft/Nvidia’s Megatron-Turing (570 billion parameters in size). We predict that GPT-4 will be trained on a dataset at least an order of magnitude larger than this—perhaps as large as 10 trillion tokens. Meanwhile, it will be smaller (i.e., fewer parameters) than Megatron-Turing. It is possible that GPT-4 will be multimodal: that is, it will be able to work with images, videos, and other data modalities in addition to text. This would mean, for example, that it could take a text prompt as input and produce an image (like DALL-E does); or take a video as input and answer questions about it via text. A multimodal GPT-4 would be a bombshell. More likely, however, GPT-4 will be a text-only model (like the previous GPT models) whose performance on language tasks will redefine the state of the art. Two language areas in which GPT-4 may demonstrate astonishing leaps in performance are memory and summarization.
?2)?We are going to start running out of data to train large language models.
It has become a cliché to say that data is the new oil. This analogy is fitting in one underappreciated way: both resources are finite and at risk of being exhausted. The area of AI for which this concern is most pressing is language models. Research efforts like DeepMind’s?Chinchilla work?have highlighted that the most effective way to build more powerful large language models (LLMs) is not to make them larger but to train them on more data. More specifically, how much more language data is there that meets an acceptable quality threshold? Much of the text data on the internet is not useful to train an LLM on. This is a challenging question to answer with precision, but according to one?research group, the world’s total stock of high-quality text data is between 4.6 trillion and 17.2 trillion tokens. This includes all the world’s books, all scientific papers, all news articles, all of Wikipedia, all publicly available code, and much of the rest of the internet, filtered for quality (e.g., webpages, blogs, social media). Another?recent estimate?puts the total figure at 3.2 trillion tokens. DeepMind’s Chinchilla model was trained on 1.4 trillion tokens.
Differently, we may be well within one order of magnitude of exhausting the world’s entire supply of useful language training data. This could prove a meaningful impediment to continued progress in language AI. Privately, many leading AI researchers and entrepreneurs are worried about this. Expect to see plenty of focus and activity in this area next year as LLM researchers seek to address the looming data shortage. One possible solution is?synthetic data, though the details about how to operationalize this are far from clear. Another idea: systematically transcribing the spoken content of the world’s meetings. As the world’s leading LLM research organization, how OpenAI deals with this challenge in its soon-to-be-announced GPT-4 research will be fascinating and illuminating to see.
3)?For the first time, some members of the general public will begin using fully driverless cars as their day-to-day mode of transportation.
After years of premature hype and unfulfilled promises in the field of autonomous vehicles, something has happened recently that?surprisingly few people?seem to have noticed: truly driverless cars have arrived. Today, as a member of the general public, you can download the Cruise app (it looks just like the Uber or Lyft app) and hail a driverless vehicle—with no one behind the wheel—to take you from Point A to Point B on the streets of San Francisco. Cruise currently only offers these driverless rides at night (between 10 pm and 5:30 am), but the company is?poised?to make the service available 24/7 throughout San Francisco. Expect this to happen within weeks. Cruise’s rival Waymo is?close behind. In 2023, robotaxi services will rapidly transition from a fascinating novelty to a viable, convenient—even mundane—way to get around the city. The number of robotaxis on the road and the number of people who use them will surge. In short, autonomous vehicles are about to enter their commercialization and scaling phase. The rollout will happen on a city-by-city basis. Beyond San Francisco, expect fully driverless services to become available to the general public in at least two more U.S. cities next year. Plausible candidate locations include Phoenix, Austin, Las Vegas, and Miami.
4) Midjourney will raise venture capital funding.
The three most prominent text-to-image AI platforms today are DALL-E from OpenAI, Stable Diffusion from Stability AI (and other contributors), and Midjourney. OpenAI?raised?$1 billion from Microsoft in 2019 and is currently?in talks?to raise billions more. Stability AI?raised?$100 million a few months ago and is already?seeking?to raise more. Midjourney, by contrast, has spurned all outside funding. The company’s usage and growth have been astonishing: as of this writing, it has nearly 6 million users and substantial revenues. Yet according to its website, Midjourney remains a “small self-funded” organization with only 11 full-time team members. Midjourney’s founder and CEO David Holz was previously the co-founder and CTO at Leap Motion, a once-high-flying virtual reality startup that raised close to $100 million in venture funding during the 2010s before crashing back down to earth and getting acquired in a fire sale. Holz’s negative experiences with his VC investors during the Leap Motion saga have allegedly convinced him not to take outside capital this time around. The many VC suitors who have sought to invest in Mid journey have so far all been rebuffed. Yet faced with the demands of blistering growth, intensifying competition, and a massive market opportunity, we predict Holz will give in and raise a large funding round for Midjourney in 2023. Otherwise, the company risks being left behind in the generative AI gold rush that it helped usher in.
?5) Search will change more in 2023 than it has since Google went mainstream in the early 2000s.
Search is the primary means by which we navigate and access digital information. It lies at the heart of the modern internet experience. Today’s large language models can read and write with a level of sophistication that a few years ago would have seemed inconceivable. This will have profound implications for how we search. In the wake of ChatGPT, one reconceptualization of search that has?gotten?a lot of attention is the idea of conversational search. Why enter a query and get back a long list of links (the current Google experience) if you could instead have a dynamic conversation with an AI agent in order to find what you are looking for?
Conversational search has a bright future. One major challenge needs to be resolved, though, before it is ready for primetime: accuracy. Conversational LLMs are not reliably accurate; they occasionally share factually untrue information with total confidence. OpenAI CEO Sam Altman himself?recently cautioned: “It’s a mistake to be relying on ChatGPT for anything important right now.” Most users will not accept a search application that is accurate 95% or even 99% of the time. Addressing this issue in a scalable and robust way will be one of the primary challenges facing search innovators in 2023. You.com, Character.AI, Metaphor, and Perplexity are among the wave of promising young startups looking to take on Google and reinvent consumer search with LLMs and conversational interfaces. But consumer internet search is not the only type of search that LLMs will transform. Enterprise search—the way that organizations search and retrieve private internal data—is likewise on the cusp of a new golden age. Thanks to large-scale vectorization, LLMs enable true?semantic search?for the first time: the ability to index and access information based on underlying concepts and context rather than simple keywords. This will make enterprise search vastly more powerful and productive. Startups like Hebbia and Glean are leading the charge to transform enterprise search using large language models. And the opportunities for next-generation search extend beyond text. Recent advances in AI open up whole new possibilities in multimodal search: that is, the ability to query and retrieve information across data modalities. Given that it accounts for ~80% of all data on the internet, no modality represents a bigger opportunity than video. Imagine being able to search effortlessly and precisely for a particular moment, individual, concept, or action within a video. Twelve Labs is one startup building a multimodal AI platform to enable nuanced video search and understanding. Search has changed surprisingly little since Google’s ascendance during the dot-com era. Next year, thanks to large language models, this will begin to change dramatically.
6) Efforts to develop humanoid robots will attract considerable attention, funding, and talent. Several new humanoid robot initiatives will launch.
The humanoid robot is perhaps the definitive symbol of Hollywood’s exaggerated, dramatized depiction of artificial intelligence (think?Ex Machina?or?I, Robot). Now, humanoid robots are fast becoming a reality. We build robots shaped like humans, for the simple reason that we have architected much of the physical world for humans. If we plan to use robots to automate complex activities in the world—in factories, shopping malls, offices, and schools—the most effective approach is often for those robots to have the same form factor as the humans that would otherwise be completing those activities. This way, robots can be deployed in diverse settings with no need for the surrounding environment to be adapted.
Tesla has catalyzed the field of humanoid robotics this year with the launch of its Optimus robot, which debuted at the company’s AI Day in September. Elon Musk?has said?that he believes the Optimus robot will eventually be worth more to Tesla than its entire car business. Tesla’s robot still has a?long way to go?before it is ready for primetime—but don’t underestimate the rapid progress that the company is capable of when it devotes its full resources to the task. A crop of promising startups is likewise moving the field of humanoid robotics forward, including Agility Robotics, Halodi Robotics, Sanctuary AI, and Collaborative Robotics. In 2023, expect more contenders to enter the fray—both new startups and established companies (e.g., Toyota, Samsung, General Motors, Panasonic)—as the race to build humanoid robots heats up. Similar to autonomous vehicles circa 2016, waves of talent and capital will start pouring into the field next year as more people come to appreciate the scale of the market opportunity.
7) The concept of “LLMOps” will emerge as a trendy new version of MLOps.
When a major new technology platform emerges, an associated need—and opportunity—arises to build tools and infrastructure to enable this new platform. Venture capitalists like to think of these supporting tools as “picks and shovels” (for the upcoming gold rush). In recent years, machine learning tooling—widely referred to as MLOps—has been one of the startup world’s hottest categories. A wave of buzzy MLOps startups has raised large sums of capital at eye-watering valuations: Weights & Biases ($200 million raised at a $1 billion valuation), Tecton ($160 million raised), Snorkel ($138 million raised at a $1 billion valuation), OctoML ($133 million raised at an $850 million valuation), to name a few. Now, we are witnessing the emergence of a new AI technology platform: large language models (LLMs). Compared to pre-LLM machine learning, large language models represent a new AI paradigm with distinct workflows, skillsets, and possibilities. The easy availability of massive trained foundation models via API or open source completely changes what it looks like to develop an AI product. A new suite of tools and infrastructure is therefore destined to?emerge. We predict the term “LLMOps” will catch on as a shorthand to refer to this new breed of AI picks and shovels. Examples of new LLMOps offerings will include, for instance: tools for foundation model fine-tuning, no-code LLM deployment, GPU access and optimization, prompt experimentation, prompt chaining, and data synthesis and augmentation.
8)?The number of research projects that build on or cite AlphaFold will surge.
DeepMind’s AlphaFold platform, first announced in late 2020, solved one of life’s great mysteries: the protein folding problem. AlphaFold is able to accurately predict the three-dimensional shape of a protein based solely on its one-dimensional amino acid sequence, a landmark achievement that had eluded human researchers for decades. Because proteins underpin nearly every important activity that happens inside every living being on earth, more deeply understanding their structures and functions opens up profound new possibilities in biology and human health: from developing life-saving therapeutics to improving agriculture, from fighting disease to investigating the origins of life. In July 2021, DeepMind open-sourced AlphaFold and?released?a database of 350,000 three-dimensional protein structures. (As a reference point, the total number of protein structures known to mankind prior to AlphaFold was around 180,000.) Then, a few months ago, DeepMind?publicly released?the structures for another 200?million?proteins—nearly all cataloged proteins known to science. Mere months after DeepMind’s latest release, more than 500,000 researchers from 190 countries have used the AlphaFold platform to access 2 million different protein structures. This is just the beginning. Breakthroughs of AlphaFold’s magnitude require years for their full impact to manifest. In 2023, expect the volume of research built on top of AlphaFold to surge. Researchers will take this vast new trove of foundational biological knowledge and apply it to produce world-changing applications across disciplines, from new vaccines to new types of plastics.
?9) DeepMind, Google Brain, and/or OpenAI will undertake efforts to build a foundation model for robotics.
The term “foundation model,”?introduced?last year by a team of Stanford researchers, refers to a massive AI model trained on broad swaths of data that, rather than being built for a specific task, can perform effectively on a wide range of different activities. Foundation models have been a key driver of recent progress in AI. Today’s foundation models are breathtakingly powerful. But—whether they are text-generating models like GPT-3, or text-to-image models like Stable Diffusion, or models for computer actions like Adept—they operate exclusively in the digital realm.
AI systems that act in the real world—e.g., autonomous vehicles, warehouse robots, drones, humanoid robots—have so far remained mostly untouched by the new foundation model paradigm. This will change in 2023. Expect early pioneering work on this concept of foundation models for robotics to come from the world’s leading AI research organizations: DeepMind, Google Brain or perhaps OpenAI What would it mean to build a foundation model for robotics—in other words, a foundation model for the physical world? At a high level, such a model might be trained on troves of data from different sensor modalities (e.g., camera, radar, lidar) in order to develop a generalized understanding of physics and real-world objects: how different objects move, how they interact with one another, how heavy or fragile or soft or flexible they are, what happens when you touch or drop or throw them. This “real-world foundation model” could then be fine-tuned for particular hardware platforms and particular downstream activities.
?10)?Many billions of dollars of new investment commitments will be announced to build chip manufacturing facilities in the United States as the U.S. makes contingency plans for Taiwan.
AI, like human intelligence, depends upon both software and hardware. Certain types of advanced semiconductors are essential to power modern AI. By far the most important and widespread of these are Nvidia’s GPUs; players like AMD, Intel, and a handful of younger AI chip upstarts are also seeking to enter the market. Nearly all of these AI chips are designed in the United States. And nearly all of them are manufactured in Taiwan. One company—the Taiwan Semiconductor Manufacturing Company (TSMC)—produces most of the world’s advanced chips,?including?Nvidia’s highly coveted GPUs. Tensions between China and Taiwan have escalated dangerously over the past year. Many observers now believe it is likely or even inevitable that China will invade and reabsorb Taiwan sometime in the next few years. This represents a major strategic dilemma for the United States, the technology world, and the field of AI. In an effort to mitigate this precarious AI hardware bottleneck and reduce its reliance on Taiwan, in 2023 the U.S. government will massively incentivize and subsidize the construction of advanced chip manufacturing facilities on American soil. The CHIPS and Science Act, passed into law this summer, provides legislative impetus and budgetary resources for this. This process is already underway. Two weeks ago, TSMC?announced?it would invest $40 billion to build two new chip manufacturing plants in Arizona. President Biden visited the Arizona site in person to hail the announcement. Importantly, the new TSMC plants—slated to begin production by 2026—will be capable of?producing 3-nanometer chips, the most advanced semiconductors in the world today. Expect to see more such commitments in 2023 as the U.S. seeks to derisk the global supply base for critical AI hardware.
?OPEN, STABILITY AI, MIDJOURNEY, GOOGLE
?OpenAI?introduced a world of?weird and wonderful mash-ups?when its text-to-image model DALL-E was released in 2021. Type in a short description of pretty much anything, and the program spat out a picture of what you asked for in seconds. DALL-E 2, unveiled in April 2022, was?a massive leap forward. Google also launched its own image-making AI, called?Imagen.?Yet the biggest game-changer was?Stable Diffusion, an open-source text-to-image model released for free by UK-based startup Stability AI in August. Not only could Stable Diffusion produce some of the most stunning images yet, but it was designed to run on a (good) home computer. A new report about how industrial design and engineering firms are using generative AI.?
By making text-to-image models accessible to all, Stability AI poured fuel on what was already an inferno of creativity and innovation. Millions of people have created tens of millions of images in just a few months. But there are problems, too. Artists are caught in the middle of?one of the biggest upheavals?in a decade. And, just like language models, text-to-image generators can amplify the biased and toxic associations buried in training data scraped from the internet. The tech is now being built into commercial software, such as Photoshop. Visual effects artists and video-game studios are exploring how it can fast-track development pipelines. And text-to-image technology has already advanced to?text-to-video. The AI-generated video clips demoed by Google, Meta, and others in the last few months are only seconds long, but that will change. One day movies could be made just by feeding a script into a computer. Nothing else in AI grabbed people’s attention more last year—for the best and worst reasons. Now we wait to see what lasting impact these tools will have on creative industries—and the entire field of AI. No one knows where the rise of generative AI will leave us.?
TOP 10 ARTIFICIAL INTELLIGENCE TRENDS THAT WILL REWRITE TECHNOLOGY IN 2023
In the near future, AI will play a significant role. In 2023, there will be a number of emerging trends in AI. The top 10 AI trends, which are those for 2023, are listed. In the year 2023, technology will be redefined by these AI trends. Amount of interest in predictive analysis.
The development of predictive analytics has become one of the most exciting fields of artificial intelligence, with applications in many academic fields. It uses data, statistical algorithms, and ML approaches to forecast the future based on past data. The objective is to accurately predict the future using historical data. The history of predictive analytics shows that it has only lately gained popularity; it did not just suddenly appear. The rate of expansion of hyperautomation
a term used to denote the expansion of traditional business process automation past the boundaries of particular operations. Hyperautomation refers to the automation of automation, the dynamic discovery of business processes, and the creation of bots to automate them. It combines artificial intelligence (AI) techniques with robotic process automation (RPA). According to Gartner, hyperautomation will gain more importance in the next years as it becomes essential for any company that wants to stay up with the development of digital technology.
Cybersecurity and AI
The increasing use of artificial intelligence (AI) in security operations is the next logical development in automated defenses against cyber threats. AI is utilized in cybersecurity to carry out normal data storage and protection tasks, going beyond the capabilities of its forerunner, automation. Artificial intelligence in cybersecurity, however, goes beyond this and helps with tasks that are more challenging. One application for advanced analytics is the detection of ongoing assaults or other ominous trends. Not all of the news is positive, though. Organizations will be playing a never-ending game of cat and mouse with cyber criminals as they employ AI to their advantage. As a result, businesses that are worried about remaining in business need to start incorporating AI into their cybersecurity as soon as possible.
AI-ENHANCED
Innovating and automating processes will use more AI and data science in 2023. Data ecosystems can grow, reduce waste, and deliver up-to-date data to a range of inputs. However, it is essential to create a basis for change and encourage creativity. Software development processes can be optimized with the help of AI, and there are additional benefits such as increased collaboration and a larger body of knowledge. To switch to a sustainable delivery model, we must promote a data-driven culture and move past the experimental phases. There is little doubt that this will represent a big development in AI. The Popularity of AIOps is Growing. The sophistication of IT systems has increased during the last few years. According to a recent Forrester prediction, vendors would look for platform solutions that provide visibility across various monitoring domains, including application, infrastructure, and networking.
With the aid of AIOps solutions and improved data analysis of the massive amounts of incoming information, IT operations and other teams may enhance their most important processes, decisions, and actions. Forrester encouraged IT leaders to seek out AIOps vendors who integrated the IT operations management toolchain, offered end-to-end digital experiences, and connected data in order to promote cross-team collaboration.
Artificial intelligence and automation (AutoML)
Two promising applications of automated machine learning are the automatic adjustment of neural network topologies and enhanced tools for data labeling. The price and time to market for new artificial intelligence (AI) products will be decreased when the selection and improvement of a neural network model are automated. In order to operationalize these models in the future, PlatformOps, MLOps, and DataOps processes will need to be improved, according to Gartner. Gartner refers to these complex aspects collectively as XOps.
Natural Language Processing Extension
As a result of the continual need for computers to better understand human languages, NLP is always growing. NLP-based solutions are offered by startups and can recognize words, sentences, and speech segments. Businesses use them to improve customer interaction and conduct in-depth research. For instance, companies in the HR, travel, and consumer goods industries utilize NLP-based smart assistants to speed response times and provide information about their products. NLP also enables machines to communicate with people in their own languages. This then spreads various language-related jobs into numerous languages, including text analytics, email filters, text prediction, and digital phone calls.
Virtual agents are introduced
Virtual agents, often known as virtual assistants, automate mundane tasks so that employees can concentrate on more important tasks. AI-enabled voice assistants replace interactions with current and potential customers, improve product discovery, and provide product recommendations. As a result, they are used in many different industries, including retail and the food industry. They also help HR teams with onboarding, resume analysis, and choosing the best candidates. In order to automate client contacts and reduce time spent on administrative tasks, entrepreneurs develop intelligent virtual assistants.
?Intelligent Quantum Systems
In a world of swift changes and judgments, it is essential to quickly and accurately analyze enormous amounts of information. Quantum AI’s development of challenging task optimization and resolution improves business operations. Due to quantum computers’ enormous processing power, high-performance AI is made possible. Advances in quantum AI enable high-speed data processing that surpasses the limitations of traditional computers. Startups develop cutting-edge quantum algorithms and intelligent quantum computers to spread the usage of quantum AI throughout industries. Industry, the biological sciences, and finance are the three key markets for quantum AI.
A cutting-edge AI system
Edge computing reduces latency, bandwidth, and energy use by bringing computations closer to data sources. By utilizing AI at the edge, developers and businesses may significantly reduce the infrastructure needs for real-time data processing. Companies apply this technology to smart factories, cities, and cars for autonomous driving systems to prevent system failure. In combination with other technologies like 5G and high-performance computing, Edge AI provides organizations with python to build models. This open-source machine learning tool can help everyone.
?CONCLUSION
we have explored all the top Artificial Intelligence Software that is available in the market. For Machine learning all the above-mentioned software is good but when compared to the others in the top 10, Azure Machine Learning Studio & H2O are much easier to use. As virtual assistants, Google, Alexa, and Cortana are equally good. Google Assistant is a virtual assistant by Google. It can be used on mobiles and smart home devices. Supported operating systems include Android, iOS, and KaiOS. Languages supported by Google Assistant are English, Hindi, Indonesian, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Dutch, Russian, and Swedish. Viv provides an AI platform for developers to distribute their products. Viv is a personal assistant developed by Siri. Blockchain is a free wallet. It is for digital currency transactions. You will be able to send, receive, and store digital currencies. Ayasdi provides AI for Finance, Healthcare, and the public sector. It provides a framework for application development that is scalable, reliable, and manageable.
We can see that a very large area of our life can be converted into AI and we save our time to do some more important work. It is both applicable to people and businesses. But, what will happen to look like magic- and it is not a dream.