Cafeto is a Colombian software development company whose mission is to provide a value-added service that gives clients a growth advantage while promoting the integral development of our professionals. As a result of our partnership with LinkedIn, we have been able to accelerate our growth in the past year, offering better and faster resources to our customers.
Before implementing LinkedIn’s solutions, our challenge was to position our brand and raise awareness of our work culture, an essential pillar for attracting talent. With LinkedIn, we have been able to find these candidates and position the culture and services to a network of nearly 8,000 professional followers.
Cafeto and LinkedIn Now
LinkedIn is now one of our main talent attraction tools for Cafeto, where we have been able to expand information, prospecting, valuable content and be a point of reference, which has contributed positively to the publication of jobs, the daily life of Cafeto, news, our services, achievements, growth, and has opened the possibility of acquiring talent.
This network has helped us to position the brand in the market, recruit qualified talent, and grow agilely as a team. As an added value, it provides us with all the necessary tools to get the required talent efficiently.
“We decided to use LinkedIn’s solutions because of the access it gives us to the best resources available in the market. Much of our growth has been leveraged on LinkedIn solutions.”
– Luis Perez, CEO, Cafeto Software.
Cafeto Makers say…
Catalina Tobón, Sales & Marketing Director: “It’s a primary tool today for B2B positioning. It brings us conversion and visits to the website. It’s a lead generation tool.”
People Acquisition team: “LinkedIn provides us with up-to-date information about the profiles we are looking for in Cafeto. We receive 6 to 7 messages a day about our job openings, which helps us acquire talent. LinkedIn contributes to our brand positioning and sharing the content of interest. It is one of the most used networks in both Colombia and Mexico, countries where our team is located.”
Today, learning Artificial Intelligence has almost become synonymous with learning to program in Python. This programming language created by Guido Van Rossum in 1991 is, by far, the most used today in artificial intelligence projects, especially in the field of ‘machine learning’.
It helps, in addition to its popularity as a generalist programming language (and also in related fields like data analysis), that all the major AI libraries (Keras, TensorFlow, SciPy, Pandas, Scikit-learn, etc.) are designed to work with Python.
However, Artificial Intelligence is much older than Python, and there were other languages that excelled in this field for decades before it landed. Let’s take a look at them:
IPL
The Information Processing Language (IPL) is a low-level language (almost as much as the assembler) that was created in 1956 in order to show that the expressor theorems in the ‘Principia Mathematica’ by mathematicians and philosophers Bertrand Russell and Alfred North Whitehead, could be tested by turning to computers.
IPL introduced into the programming features that are still entirely in force today, such as symbols, recursion, or the use of lists. The latter, a data type so flexible that it, in turn, allowed a list to be entered as an element of another list (which in turn could join another list as an element, etc.). It was essential when using it to develop the first programs of AI, such as Logic Theorist (1956) or the NSS chess program (1958).
Despite its importance in the history of AI languages, several factors (the first being the complexity of its syntax) caused it to be quickly replaced by the following language on the list.
LISP
LISP is the oldest programming language dedicated to artificial intelligence among those still in use; it is also the second high-level programming language in history. It was created in 1958 (one year after FORTRAN and one before COBOL) by John McCarthy, who two years before had already been responsible for coining the term ‘artificial intelligence ‘.
McCarthy had shortly before developed a language called FLPL (FORTRAN List Processing Language), an extension of FORTRAN, and decided to compile in the same language the high-level nature of FLPL, all the novelties provided by IPL, and the formal system known as lambda calculus. The result was named LISP (from ‘LISt Processor’).
At the same time, he was developing FLPL. McCarthy was also formulating so-called ‘alpha-beta pruning’, a search technique that reduces the number of nodes evaluated in a game tree. And, to implement it, he introduced such a fundamental element in programming as if-then-else structures.
Programmers quickly fell in love with the freedom offered by the flexibility of this language, and with its role as a prototyping tool. Thus, for the next quarter-century, LISP became the reference language in the field of AI. Over time, LISP fragmented into a whole series of ‘dialects’ still valid in various computing fields, such as Common LISP, EMACS LISP, Clojure, Scheme, or Racket.
The following example illustrates a LISP function to calculate the factorial of a number. In the code snippet, notice the use of recursion to calculate the factorial (calling factorial within the factorial function). This function could have been invoked with (factorial 9).
In 1968, Terry Winograd developed a state-of-the-art program at LISP called SHRDLU that could interact with a user in a natural language. The program represented a world block.
The user could interact with that world, directing the program to consult and interact with the world using statements such as “lift the red block“ or “can a pyramid be supported by a block?” This demonstration of natural language planning and understanding within a simple physics-based blocky world provided considerable optimism for AI and the LISP language.
PROLOG
The PROLOG language (from the French ‘Programmation en logique’), was born at a difficult time for the development of artificial intelligence; on the threshold of the first ‘AI Winter’. A time when the initial fury for the applications of this technology crashed against skepticism caused by the lack of progress, which generated public and private disinvestment in its development.
Specifically, it was created in 1972 by the French computer engineering professor, Alain Colmeraurer to introduce Horn’s clauses, a formula of propositional logic, into software development. Although globally, it never became as widely used as LISP, it did become the primary language of AI development in its home continent (as well as in Japan).
Being a language based on the declarative programming paradigm – like LISP, on the other hand – its syntax is very different from that of typical imperative programming languages such as Python, Java, or C ++.
PROLOGUE’s ease in handling recursive methods and pattern matching caused IBM to bet on implementing PROLOG in its IBM Watson for natural language processing tasks.
PROLOG code example in the IDE SWI-Prolog.
The last 60 years have seen significant changes in computing architectures and advances in AI techniques and applications. Those years have also seen an evolution of AI languages, each one with its own functions and approaches to problem-solving. But today, with the introduction of big data and new processing architectures that include CPUs clustered with GPU arrays, the foundation has been laid for a host of innovations in AI and the languages that power it.
How cool is it to know this info about AI languages, right? But we have something even cooler for you! And this is an amazing opportunity to join Silicon Valley’s top IT companies. 👇
The Healthcare industry is going through a transformation, COVID-19 is the main reason why these opportunities for tech innovation are happening faster and becoming a big change in both industries.
Next, you’ll find three opportunities to achieve tech innovation and grow in both industries. Virtual healthcare became an extremely relevant service during the pandemic as it gave space for creating new ways to care and educate patients and staff.
TeleEducation in Healthcare
There must be systems in place for healthcare providers to be able to respond to a crisis, like in recent events. Being able to train personnel fast is one of the lessons that Covid 19 left.
Online education is extremely easy since all the material is digital and accessible. With the right tools provided, you can reduce the need to conduct face-to-face meetings and save time.
Edtech helps accelerate positive outcomes in student equity, student-teacher relationships, and systemic alignment. Therefore, the training process for personnel has to supply: videoconferencing, case presentations, pre-recorded lectures, and roundtables.
But this implies health care education has to improve in certain aspects such as:
Scalable online simulations that can be processed by old and modern computer models.
Simple, user-friendly hardware and software interfaces.
Easy-to-use and portable telepresence systems.
Remote 3D visualization.
Flexibility in the healthcare workforce.
Creating a system to keep track of personnel’s work should be beneficial for healthcare providers and stay engaged. Is important to improve their quality of life, considering the vast number of hours they endure away from their families and other activities.
It prevents full-time staff from taking on an excessive workload, preventing burnout and lowering turnover. Talent maintains the flexibility they want per day, which allows them to show well-rested and motivation. With a fully engaged team, patient outcomes improve.
Workforce flexibility, therefore, cannot be defined as a particular type of reform, it should be understood as the capacity of a team to respond and adapt to task distribution to establish the extent of multiskilling and role overlap.
Opportunity to build caregivers hiring capacity.
Healthcare staff could be overwhelmed with the amount of work. On that account, providers should find people who can care for others without having extensive years of education or training. Creating a volunteering system that can provide the right person to the patients’ needs.
A caregiver helps people who can’t take care of themselves. The person who needs help may be a child, an adult, or an older adult. The patient may need help because of an injury, disability, or they may have a chronic illness.
Caregiving is a wide-ranging spectrum that covers all kinds of care. They assist with activities of daily living and medical tasks too.
Helping with daily tasks like bathing, eating, and taking medicine.
Doing housework and cooking.
Running errands.
Driving the person to appointments.
Providing company and emotional support.
Arranging activities and medical care.
Making health and financial decisions.
Therefore, these opportunities for tech innovation in healthcare are beyond creating just an online surface and providing health services. Health care companies should grow in technology, but it can’t happen if they don’t have enough personnel, giving the best care for patients, and keeping the organization in order.
